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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-10-21
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">153</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">20</span>
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                <span id="display-count" class="font-medium">显示 153 篇论文 (共 153 篇)</span>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17535v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>角色扮演如何影响零样本大语言模型排序器的相关性判断
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            How role-play shapes relevance judgment in zero-shot LLM rankers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yumeng Wang, Jirui Qi, Catherine Chen, Panagiotis Eustratiadis, Suzan Verberne
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究零样本LLM排序器中角色扮演提示对相关性判断的影响机制。核心发现是通过因果干预技术揭示角色描述信息主要在模型早期层编码，与任务指令在中层交互，而与查询文档表征交互有限，识别出负责角色条件相关性的关键注意力头。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接研究LLM在搜索排序中的零样本应用，深入分析提示工程中的角色扮演机制，对搜索和推荐系统的提示设计具有重要指导意义。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 13:39:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17535v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17535v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) have emerged as promising zero-shot rankers, but their performance is highly sensitive to prompt formulation. In particular, role-play prompts, where the model is assigned a functional role or identity, often give more robust and accurate relevance rankings. However, the mechanisms and diversity of role-play effects remain underexplored, limiting both effective use and interpretability. In this work, we systematically examine how role-play variations influence zero-shot LLM rankers. We employ causal intervention techniques from mechanistic interpretability to trace how role-play information shapes relevance judgments in LLMs. Our analysis reveals that (1) careful formulation of role descriptions have a large effect on the ranking quality of the LLM; (2) role-play signals are predominantly encoded in early layers and communicate with task instructions in middle layers, while receiving limited interaction with query or document representations. Specifically, we identify a group of attention heads that encode information critical for role-conditioned relevance. These findings not only shed light on the inner workings of role-play in LLM ranking but also offer guidance for designing more effective prompts in IR and beyond, pointing toward broader opportunities for leveraging role-play in zero-shot applications.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17354v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>面向通用检索增强生成的混合模态检索研究
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Towards Mixed-Modal Retrieval for Universal Retrieval-Augmented Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenghao Zhang, Guanting Dong, Xinyu Yang, Zhicheng Dou
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究混合模态环境下的通用检索增强生成问题，核心思想是构建统一的多模态到多模态检索器，并通过自动化数据生成和VLM反馈对齐来优化检索结果与生成任务的匹配度。</p>
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接解决混合模态检索增强生成的核心挑战，提出的统一检索器架构和VLM反馈对齐方法对搜索和推荐系统的多模态信息处理具有重要参考价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 09:56:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17354v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17354v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span><span class="category-tag">cs.LG</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) by retrieving relevant documents from an external corpus. However, existing RAG systems primarily focus on unimodal text documents, and often fall short in real-world scenarios where both queries and documents may contain mixed modalities (such as text and images). In this paper, we address the challenge of Universal Retrieval-Augmented Generation (URAG), which involves retrieving and reasoning over mixed-modal information to improve vision-language generation. To this end, we propose Nyx, a unified mixed-modal to mixed-modal retriever tailored for URAG scenarios. To mitigate the scarcity of realistic mixed-modal data, we introduce a four-stage automated pipeline for generation and filtering, leveraging web documents to construct NyxQA, a dataset comprising diverse mixed-modal question-answer pairs that better reflect real-world information needs. Building on this high-quality dataset, we adopt a two-stage training framework for Nyx: we first perform pre-training on NyxQA along with a variety of open-source retrieval datasets, followed by supervised fine-tuning using feedback from downstream vision-language models (VLMs) to align retrieval outputs with generative preferences. Experimental results demonstrate that Nyx not only performs competitively on standard text-only RAG benchmarks, but also excels in the more general and realistic URAG setting, significantly improving generation quality in vision-language tasks.
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</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17245v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>基于扩散模型的推荐系统效率与效果权衡研究
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            On Efficiency-Effectiveness Trade-off of Diffusion-based Recommenders
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenyu Mao, Jiancan Wu, Guoqing Hu, Wei Ji, Xiang Wang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究扩散模型在序列推荐中因多步去噪过程导致的效率与效果权衡问题；核心方法是提出TA-Rec两阶段框架，通过时序一致性正则化平滑去噪函数实现一步生成，并基于偏好相似性自适应对齐用户偏好。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统中的扩散模型效率-效果权衡问题，提出了两阶段训练框架，属于核心领域进展和直接LLM应用范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 07:35:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17245v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17245v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Diffusion models have emerged as a powerful paradigm for generative sequential recommendation, which typically generate next items to recommend guided by user interaction histories with a multi-step denoising process. However, the multi-step process relies on discrete approximations, introducing discretization error that creates a trade-off between computational efficiency and recommendation effectiveness. To address this trade-off, we propose TA-Rec, a two-stage framework that achieves one-step generation by smoothing the denoising function during pretraining while alleviating trajectory deviation by aligning with user preferences during fine-tuning. Specifically, to improve the efficiency without sacrificing the recommendation performance, TA-Rec pretrains the denoising model with Temporal Consistency Regularization (TCR), enforcing the consistency between the denoising results across adjacent steps. Thus, we can smooth the denoising function to map the noise as oracle items in one step with bounded error. To further enhance effectiveness, TA-Rec introduces Adaptive Preference Alignment (APA) that aligns the denoising process with user preference adaptively based on preference pair similarity and timesteps. Extensive experiments prove that TA-Rec's two-stage objective effectively mitigates the discretization errors-induced trade-off, enhancing both efficiency and effectiveness of diffusion-based recommenders.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17800v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Glyph：通过视觉-文本压缩扩展上下文窗口
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Glyph: Scaling Context Windows via Visual-Text Compression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何解决LLM长上下文窗口带来的计算和内存成本问题。核心方法是将长文本渲染为图像，利用视觉语言模型进行压缩处理，通过遗传搜索优化视觉渲染配置来平衡精度和压缩率。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文通过视觉-文本压缩方法扩展上下文窗口，直接涉及Transformer效率提升和VLM多模态建模，对推荐系统和搜索中的长序列处理具有重要应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:58:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17800v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17800v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) increasingly rely on long-context modeling for tasks such as document understanding, code analysis, and multi-step reasoning. However, scaling context windows to the million-token level brings prohibitive computational and memory costs, limiting the practicality of long-context LLMs. In this work, we take a different perspective-visual context scaling-to tackle this challenge. Instead of extending token-based sequences, we propose Glyph, a framework that renders long texts into images and processes them with vision-language models (VLMs). This approach substantially compresses textual input while preserving semantic information, and we further design an LLM-driven genetic search to identify optimal visual rendering configurations for balancing accuracy and compression. Through extensive experiments, we demonstrate that our method achieves 3-4x token compression while maintaining accuracy comparable to leading LLMs such as Qwen3-8B on various long-context benchmarks. This compression also leads to around 4x faster prefilling and decoding, and approximately 2x faster SFT training. Furthermore, under extreme compression, a 128K-context VLM could scale to handle 1M-token-level text tasks. In addition, the rendered text data benefits real-world multimodal tasks, such as document understanding. Our code and model are released at https://github.com/thu-coai/Glyph.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17705v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>上下文注意力调制：面向大型语言模型的高效多任务适应
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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        <div class="mb-2 text-base text-gray-700">
            Contextual Attention Modulation: Towards Efficient Multi-Task Adaptation in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dayan Pan, Zhaoyang Fu, Jingyuan Wang, Xiao Han, Yue Zhu, Xiangyu Zhao
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究大语言模型在多任务适应中平衡知识保留与任务专业化的问题，核心方法是提出上下文注意力调制机制动态调节自注意力表示，并通过混合框架结合共享和专用模块实现自适应知识融合。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的上下文注意力调制机制和混合框架直接针对LLM多任务适应效率问题，属于Transformer架构效率和LLM应用的核心前沿技术，与推荐搜索系统的多任务优化高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:19:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17705v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17705v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) possess remarkable generalization capabilities but struggle with multi-task adaptation, particularly in balancing knowledge retention with task-specific specialization. Conventional fine-tuning methods suffer from catastrophic forgetting and substantial resource consumption, while existing parameter-efficient methods perform suboptimally in complex multi-task scenarios. To address this, we propose Contextual Attention Modulation (CAM), a novel mechanism that dynamically modulates the representations of self-attention modules in LLMs. CAM enhances task-specific features while preserving general knowledge, thereby facilitating more effective and efficient adaptation. For effective multi-task adaptation, CAM is integrated into our Hybrid Contextual Attention Modulation (HyCAM) framework, which combines a shared, full-parameter CAM module with multiple specialized, lightweight CAM modules, enhanced by a dynamic routing strategy for adaptive knowledge fusion. Extensive experiments on heterogeneous tasks, including question answering, code generation, and logical reasoning, demonstrate that our approach significantly outperforms existing approaches, achieving an average performance improvement of 3.65%. The implemented code and data are available to ease reproducibility at https://github.com/Applied-Machine-Learning-Lab/HyCAM.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17483v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>ReXMoE：以最小开销在专家混合模型中重用专家
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            ReXMoE: Reusing Experts with Minimal Overhead in Mixture-of-Experts
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zheyue Tan, Zhiyuan Li, Tao Yuan, Dong Zhou, Weilin Liu, Yueqing Zhuang, Yadong ...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究层局部路由机制限制MoE架构专家组合灵活性的问题；核心思想是允许路由器跨相邻层复用专家，解耦专家维度与层预算，通过渐进式扩展路由策略实现更丰富的专家组合。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出跨层专家复用的MoE架构创新，直接提升Transformer效率并增强模型表达能力，对大规模推荐系统的参数效率优化具有重要价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 12:27:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17483v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17483v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Mixture-of-Experts (MoE) architectures have emerged as a promising approach to scale Large Language Models (LLMs). MoE boosts the efficiency by activating a subset of experts per token. Recent works show that fine-grained experts substantially enriches the combinatorial flexibility of active experts and enhances model expressiveness. However, such a design is fundamentally limited by the layer-local routing mechanism: each layer is restricted to its own expert pool. This requires a careful trade-off between expert dimensionality and routing diversity given fixed parameter budgets. We describe ReXMoE, a novel MoE architecture that improves routing beyond the existing layer-local approaches by allowing routers to reuse experts across adjacent layers. ReXMoE decouples expert dimensionality from per-layer budgets, enabling richer expert combinations without sacrificing individual expert capacity or inflating overall parameters. To this end, we propose a new progressive scaling routing (PSR) strategy to gradually increase the candidate expert pool during training. As a result, ReXMoE improves both language modeling and downstream task performance. Extensive experiments on models ranging from 0.5B to 7B parameters across different architectures demonstrate that ReXMoE consistently improves performance under fixed architectural dimensions, confirming ReXMoE as new design paradigm for parameter-efficient and scalable MoE-based LLMs.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17132v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>大语言模型能否识别您的潜在偏好？个性化交互中潜在信息发现的基准研究
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Do LLMs Recognize Your Latent Preferences? A Benchmark for Latent Information Discovery in Personalized Interaction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ioannis Tsaknakis, Bingqing Song, Shuyu Gan, Dongyeop Kang, Alfredo Garcia, Gaow...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM能否通过对话发现用户的潜在偏好信息，核心方法是建立包含多任务的三智能体评估框架来系统测试LLM的潜在信息发现能力。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对LLM在个性化推荐中的核心挑战——潜在偏好发现，建立了系统评估框架，与个性化推荐和搜索高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 03:58:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17132v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17132v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) excel at producing broadly relevant text, but this generality becomes a limitation when user-specific preferences are required, such as recommending restaurants or planning travel. In these scenarios, users rarely articulate every preference explicitly; instead, much of what they care about remains latent, waiting to be inferred. This raises a fundamental question: Can LLMs uncover and reason about such latent information through conversation? We address this problem by introducing a unified benchmark for evaluating latent information discovery - the ability of LLMs to reveal and utilize hidden user attributes through multi-turn interaction. The benchmark spans three progressively realistic settings: the classic 20 Questions game, Personalized Question Answering, and Personalized Text Summarization. All tasks share a tri-agent framework (User, Assistant, Judge) enabling turn-level evaluation of elicitation and adaptation. Our results reveal that while LLMs can indeed surface latent information through dialogue, their success varies dramatically with context: from 32% to 98%, depending on task complexity, topic, and number of hidden attributes. This benchmark provides the first systematic framework for studying latent information discovery in personalized interaction, highlighting that effective preference inference remains an open frontier for building truly adaptive AI systems.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17614v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>OG-Rank：基于不确定性和奖励趋势引导自适应探索的快速与慢速学习排序
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            OG-Rank: Learning to Rank Fast and Slow with Uncertainty and Reward-Trend Guided Adaptive Exploration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Praphul Singh, Corey Barrett, Sumana Srivasta, Irfan Bulu, Sri Gadde, Krishnaram...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究如何在保证低延迟的前提下实现有效的重排序；核心方法是使用单解码器架构，通过不确定性门控机制仅在列表模糊时生成解释，并采用课程学习集中处理困难案例。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出低延迟解码器重排序方法，结合不确定性门控和课程学习，直接应用于搜索和推荐系统的排序任务，与核心领域高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:00:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17614v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17614v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Clinicians need ranking systems that work in real time and still justify their choices. Motivated by the need for a low-latency, decoder-based reranker, we present OG-Rank, a single-decoder approach that pairs a pooled first-token scoring signal with an uncertainty-gated explanation step. The model scores all candidates in one pass and generates a brief, structured rationale only when the list is genuinely ambiguous, keeping latency predictable. Trained with a curriculum that concentrates effort on hard cases, OG-Rank delivers strong effectiveness on encounter-scoped order selection (fast path: Recall@1~0.45, nDCG@20~0.625) and improves further when the gate activates (Recall@1~0.56, nDCG@20~0.699 at a 45\% gate rate), while compact backbones show similar gains under the same policy. Encoder baselines trail in both effectiveness and flexibility. The result is a practical recipe: rank fast by default and explain when it helps, a pattern that applies broadly to decision tasks where selective generation buys accuracy at acceptable cost. The single-policy design simplifies deployment and budget planning, and the curriculum principle (spend more on the hard cases, less on the easy ones) readily transfers beyond clinical order selection.
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17139v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>重新思考用于查询增强的在线策略优化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Rethinking On-policy Optimization for Query Augmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhichao Xu, Shengyao Zhuang, Xueguang Ma, Bingsen Chen, Yijun Tian, Fengran Mo, ...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM在信息检索中的查询增强问题，核心思想是提出一种混合方法OPQE，让LLM策略学习生成能最大化检索性能的伪文档，融合提示工程的灵活性和强化学习的针对性优化。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接研究LLM在信息检索中的查询增强应用，提出混合方法结合提示工程和强化学习优化检索性能，与搜索和推荐系统高度相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 04:16:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17139v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17139v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.IR</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in large language models (LLMs) have led to a surge of interest in query augmentation for information retrieval (IR). Two main approaches have emerged. The first prompts LLMs to generate answers or pseudo-documents that serve as new queries, relying purely on the model's parametric knowledge or contextual information. The second applies reinforcement learning (RL) to fine-tune LLMs for query rewriting, directly optimizing retrieval metrics. While having respective advantages and limitations, the two approaches have not been compared under consistent experimental conditions. In this work, we present the first systematic comparison of prompting-based and RL-based query augmentation across diverse benchmarks, including evidence-seeking, ad hoc, and tool retrieval. Our key finding is that simple, training-free query augmentation often performs on par with, or even surpasses, more expensive RL-based counterparts, especially when using powerful LLMs. Motivated by this discovery, we introduce a novel hybrid method, On-policy Pseudo-document Query Expansion (OPQE), which, instead of rewriting a query, the LLM policy learns to generate a pseudo-document that maximizes retrieval performance, thus merging the flexibility and generative structure of prompting with the targeted optimization of RL. We show OPQE outperforms both standalone prompting and RL-based rewriting, demonstrating that a synergistic approach yields the best results. Our implementation is made available to facilitate reproducibility.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17638v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>LLM作为先知：通过先知竞技场理解预测智能
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            LLM-as-a-Prophet: Understanding Predictive Intelligence with Prophet Arena
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qingchuan Yang, Simon Mahns, Sida Li, Anri Gu, Jibang Wu, Haifeng Xu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM预测现实世界未来事件的能力问题，核心方法是构建Prophet Arena评估框架，通过分解预测流程阶段来系统分析LLM的预测智能瓶颈。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文系统研究LLM在预测任务中的能力，直接涉及LLM在搜索推荐广告中的核心应用场景——预测用户行为和未来趋势。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:20:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17638v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17638v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Forecasting is not only a fundamental intellectual pursuit but also is of significant importance to societal systems such as finance and economics. With the rapid advances of large language models (LLMs) trained on Internet-scale data, it raises the promise of employing LLMs to forecast real-world future events, an emerging paradigm we call "LLM-as-a-Prophet". This paper systematically investigates such predictive intelligence of LLMs. To this end, we build Prophet Arena, a general evaluation benchmark that continuously collects live forecasting tasks and decomposes each task into distinct pipeline stages, in order to support our controlled and large-scale experimentation. Our comprehensive evaluation reveals that many LLMs already exhibit impressive forecasting capabilities, reflected in, e.g., their small calibration errors, consistent prediction confidence and promising market returns. However, we also uncover key bottlenecks towards achieving superior predictive intelligence via LLM-as-a-Prophet, such as LLMs' inaccurate event recalls, misunderstanding of data sources and slower information aggregation compared to markets when resolution nears.
                </div>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17238v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>StreamingThinker：大型语言模型能够在阅读过程中进行思考
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            StreamingThinker: Large Language Models Can Think While Reading
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Junlong Tong, Yingqi Fan, Anhao Zhao, Yunpu Ma, Xiaoyu Shen
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究LLM在完整输入前无法开始推理导致的延迟问题，核心思想是设计流式思考范式，让模型在输入过程中同步进行推理，通过流式CoT生成、注意力约束和并行KV缓存实现实时推理。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的流式思考范式直接针对LLM推理效率问题，通过实时推理和并行架构优化延迟，对搜索和推荐系统中的实时响应场景具有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 07:27:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17238v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17238v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) have demonstrated remarkable capabilities in chain of thought (CoT) reasoning. However, the current LLM reasoning paradigm initiates thinking only after the entire input is available, which introduces unnecessary latency and weakens attention to earlier information in dynamic scenarios. Inspired by human cognition of thinking while reading, we first design a \textit{\textbf{streaming thinking}} paradigm for LLMs, where reasoning unfolds in the order of input and further adjusts its depth once reading is complete. We instantiate this paradigm with \textit{StreamingThinker}, a framework that enables LLMs to think while reading through the integration of streaming CoT generation, streaming-constraint training, and streaming parallel inference. Specifically, StreamingThinker employs streaming reasoning units with quality control for CoT generation, enforces order-preserving reasoning through streaming attention masks and position encoding, and leverages parallel KV caches that decouple input encoding from reasoning generation, thereby ensuring alignment and enabling true concurrency. We evaluate StreamingThinker on the Qwen3 model family across math reasoning, logical reasoning, and context-based QA reasoning tasks. Experimental results show that the StreamingThinker preserves performance comparable to batch thinking, while yielding an 80\% reduction in token waiting before the onset of reasoning and a more than 60\% reduction in time-level latency for producing the final answer, demonstrating the effectiveness of the streaming paradigm for LLM reasoning. Code will be released at \href{https://github.com/EIT-NLP/StreamingLLM/tree/main/StreamingThinker}{this repository.}
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17206v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>软掩码扩散语言模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Soft-Masked Diffusion Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Michael Hersche, Samuel Moor-Smith, Thomas Hofmann, Abbas Rahimi
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究扩散语言模型中二进制掩码决策导致预测信息丢失的问题，核心创新是引入软掩码方法，将掩码标记嵌入与前一解码步骤的top-k预测标记嵌入动态融合，从而保留部分预测信息并改善上下文传播。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种改进扩散语言模型解码过程的新方法，通过软掩码机制保留预测信息，直接提升了语言模型的核心生成能力，对搜索和推荐系统中的文本生成具有重要价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 06:42:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17206v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17206v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Diffusion models have demonstrated strong potential in language modeling, offering various advantages over traditional autoregressive approaches. Their ability to generate and revise entire responses in parallel enables faster generation and built-in self-correction mechanisms. Most modern diffusion-based language models employ masked diffusion, where decoding involves iteratively processing masked tokens based on a binary decision: either retaining the mask or replacing it with the predicted token. However, this binary choice discards valuable predictive information when the mask is retained. To address this limitation, we introduce soft-masking (SM), a novel method that dynamically blends the embedding of the mask token with the embeddings of the top-$k$ predicted tokens from the previous decoding step, for each retained mask. This provides the model with a more informative prior, preserving context from earlier computations and allowing partial information about masked tokens to propagate beyond a single step. We propose a training methodology that adapts a pretrained masked diffusion language model to incorporate SM. We demonstrate that continuing pretraining a 169M parameter model with SM leads to improved perplexity and MAUVE scores. Furthermore, we finetune two state-of-the-art diffusion models, Dream-7B and Dream-Coder-7B, with SM. SM consistently improves performance across multiple coding benchmarks, particularly in high-throughput settings.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17196v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>理解并改进分层稀疏注意力模型中的长度泛化能力
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Understanding and Improving Length Generalization in Hierarchical Sparse Attention Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiaqi Leng, Xiang Hu, Junxiong Wang, Jianguo Li, Wei Wu, Yucheng Lu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">研究分块稀疏注意力模型在长上下文处理中的泛化能力问题，提出三个关键设计原则：表达性分块编码器、旁路残差路径和训练期间强制选择稀疏性，以稳定整合全局信息并弥合训练测试分布差距。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文系统分析分块稀疏注意力的核心设计原则，直接针对Transformer架构效率改进，对长序列推荐和搜索系统具有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 06:17:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17196v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17196v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Effectively processing long contexts is a critical challenge for language models. While standard Transformers are limited by quadratic complexity and poor length extrapolation, alternative architectures like sliding window attention and state space models sacrifice the ability to effectively utilize the full context due to their fixed-size memory. Chunk-based sparse attention has emerged as a promising paradigm for extreme length generalization, yet the key architectural principles underpinning its success are not yet fully understood. In this work, we present a systematic dissection of these models to identify the core components driving their performance. Through a unified framework and comprehensive ablation studies, we demonstrate that a combination of three design principles is critical: (1) an expressive, non-linear Chunk Encoder with a dedicated CLS token to produce representations for retrieval; (2) a Bypassing Residual Path to stably integrate retrieved global information without it being overridden by the local residual stream; and (3) enforced selection sparsity during pre-training to bridge the train-test distribution gap. We provide a theoretical motivation for intra-chunk information processing and landmark generation. By combining these principles, we establish a new state-of-the-art for training-free length extrapolation, successfully generalizing models trained on a 4K context to 32 million tokens on RULER and BABILong. Our findings provide a clear and empirically-grounded set of design principles for developing future, highly-capable long-context language models.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17115v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>DVAGen：动态词汇增强生成
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            DVAGen: Dynamic Vocabulary Augmented Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wei Du, Nuowei Liu, Jie Wang, Jiahao Kuang, Tao Ji, Xiaoling Wang, Yuanbin Wu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究固定词汇表语言模型难以处理新词和词汇组合的问题，核心方法是开发统一的动态词汇增强框架，通过模块化设计和现代LLM集成来提升词汇扩展的灵活性和推理效率。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的动态词汇增强框架直接解决LLM处理新词和OOV词汇的核心限制，其模块化设计和推理优化对搜索推荐系统的词汇扩展有重要应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 03:09:24
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17115v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17115v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Language models trained with a fixed vocabulary struggle to generalize to novel or out-of-vocabulary words, limiting their flexibility in handling diverse token combinations. Existing dynamic vocabulary approaches attempt to address this limitation but face challenges such as fragmented codebases, lack of support for modern LLMs, and limited inference scalability. To overcome these issues, we introduce DVAGen, a fully open-source, unified framework designed for training, evaluation, and visualization of dynamic vocabulary-augmented language models. Our framework modularizes the pipeline for ease of customization, integrates seamlessly with open-source LLMs, and is the first to provide both CLI and WebUI tools for real-time result inspection. We validate the effectiveness of dynamic vocabulary methods on modern LLMs and demonstrate support for batch inference, significantly improving inference throughput.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17498v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>深度自演进推理
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Deep Self-Evolving Reasoning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zihan Liu, Shun Zheng, Xumeng Wen, Yang Wang, Jiang Bian, Mao Yang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何提升小规模开放权重模型在复杂任务上的推理能力，其核心思想是将迭代推理建模为马尔可夫链，通过并行运行多个自我演化过程来放大微小的改进概率，从而渐进逼近正确答案。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的概率化迭代推理框架DSER虽然主要针对数学推理任务，但其核心思想——通过并行多轮弱验证的自我演化过程来提升模型推理能力——可直接迁移到推荐系统的序列决策和多轮交互优化中。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 12:51:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17498v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17498v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Long-form chain-of-thought reasoning has become a cornerstone of advanced reasoning in large language models. While recent verification-refinement frameworks have enabled proprietary models to solve Olympiad-level problems, their effectiveness hinges on strong, reliable verification and correction capabilities, which remain fragile in open-weight, smaller-scale models. This work demonstrates that even with weak verification and refinement capabilities on hard tasks, the reasoning limits of such models can be substantially extended through a probabilistic paradigm we call Deep Self-Evolving Reasoning (DSER). We conceptualize iterative reasoning as a Markov chain, where each step represents a stochastic transition in the solution space. The key insight is that convergence to a correct solution is guaranteed as long as the probability of improvement marginally exceeds that of degradation. By running multiple long-horizon, self-evolving processes in parallel, DSER amplifies these small positive tendencies, enabling the model to asymptotically approach correct answers. Empirically, we apply DSER to the DeepSeek-R1-0528-Qwen3-8B model. On the challenging AIME 2024-2025 benchmark, DSER solves 5 out of 9 previously unsolvable problems and boosts overall performance, enabling this compact model to surpass the single-turn accuracy of its 600B-parameter teacher through majority voting. Beyond its immediate utility for test-time scaling, the DSER framework serves to diagnose the fundamental limitations of current open-weight reasoners. By clearly delineating their shortcomings in self-verification, refinement, and stability, our findings establish a clear research agenda for developing next-generation models with powerful, intrinsic self-evolving capabilities.
                </div>
            </details>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17491v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>赋能现实世界：关于LLM驱动的行业智能体的技术、实践与评估综述
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yihong Tang, Kehai Chen, Liang Yue, Jinxin Fan, Caishen Zhou, Xiaoguang Li, Yuya...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究如何将通用LLM智能体技术转化为驱动行业变革的生产力，核心思想是通过记忆、规划和工具使用三大技术支柱构建从流程执行系统到自适应社会系统的行业智能体能力成熟度框架。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文系统综述了LLM驱动的行业智能体技术，虽然不聚焦于推荐系统或搜索广告，但其对智能体规划、记忆和工具使用等核心技术的深入分析对构建复杂推荐和广告系统具有重要参考价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 12:46:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17491v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17491v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With the rise of large language models (LLMs), LLM agents capable of autonomous reasoning, planning, and executing complex tasks have become a frontier in artificial intelligence. However, how to translate the research on general agents into productivity that drives industry transformations remains a significant challenge. To address this, this paper systematically reviews the technologies, applications, and evaluation methods of industry agents based on LLMs. Using an industry agent capability maturity framework, it outlines the evolution of agents in industry applications, from "process execution systems" to "adaptive social systems." First, we examine the three key technological pillars that support the advancement of agent capabilities: Memory, Planning, and Tool Use. We discuss how these technologies evolve from supporting simple tasks in their early forms to enabling complex autonomous systems and collective intelligence in more advanced forms. Then, we provide an overview of the application of industry agents in real-world domains such as digital engineering, scientific discovery, embodied intelligence, collaborative business execution, and complex system simulation. Additionally, this paper reviews the evaluation benchmarks and methods for both fundamental and specialized capabilities, identifying the challenges existing evaluation systems face regarding authenticity, safety, and industry specificity. Finally, we focus on the practical challenges faced by industry agents, exploring their capability boundaries, developmental potential, and governance issues in various scenarios, while providing insights into future directions. By combining technological evolution with industry practices, this review aims to clarify the current state and offer a clear roadmap and theoretical foundation for understanding and building the next generation of industry agents.
                </div>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17426v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>导航对齐-校准权衡：通过模型合并实现帕累托更优前沿
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Navigating the Alignment-Calibration Trade-off: A Pareto-Superior Frontier via Model Merging
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tiancheng Hu, Benjamin Minixhofer, Nigel Collier
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究大模型对齐过程中出现的校准损失问题，核心方法是通过在原始模型和对齐后模型之间进行权重插值，发现帕累托最优的模型版本，既能提升准确性又能恢复校准性能。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文通过模型权重插值解决对齐过程中的校准损失问题，虽然不直接涉及推荐系统，但其处理模型可靠性和多样性的方法对可信推荐系统具有重要借鉴意义。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 11:12:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17426v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17426v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The "alignment tax" of post-training is typically framed as a drop in task accuracy. We show it also involves a severe loss of calibration, making models overconfident, less reliable, and model outputs less diverse. We show that this trade-off can be navigated effectively via a simple post-hoc intervention: interpolating between a model's weights before and after alignment. Crucially, this is not a strict trade-off. We find that the process consistently reveals Pareto-optimal interpolations - models that improve accuracy beyond both parents while substantially recovering the calibration lost during alignment. Our work demonstrates that simple model merging provides a computationally efficient method for mitigating the full scope of the alignment tax, yielding models that are more capable and more reliable.
                </div>
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</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17555v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>语言混淆门：通过模型自蒸馏实现语言感知解码
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Language Confusion Gate: Language-Aware Decoding Through Model Self-Distillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Collin Zhang, Fei Huang, Chenhan Yuan, Junyang Lin
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究LLM在多语言文本生成中的语言混淆问题，核心方法是设计轻量级语言混淆门插件，通过自蒸馏训练的语言家族预测在解码阶段选择性屏蔽不当语言token，实现精准的多语言控制。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的语言混淆门技术通过解码时过滤和自蒸馏方法解决LLM多语言生成问题，虽然不直接针对推荐系统，但其轻量级插件架构和token级控制技术对搜索和广告中的多语言内容生成有潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:02:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17555v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17555v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) often experience language confusion, which is the unintended mixing of languages during text generation. Current solutions to this problem either necessitate model retraining or cannot differentiate between harmful confusion and acceptable code-switching. This paper introduces the Language Confusion Gate (LCG), a lightweight, plug-in solution that filters tokens during decoding without altering the base LLM. The LCG is trained using norm-adjusted self-distillation to predict appropriate language families and apply masking only when needed. Our method is based on the findings that language confusion is infrequent, correct-language tokens are usually among the top predictions, and output token embedding norms are larger for high-resource languages, which biases sampling. When evaluated across various models, including Qwen3, GPT-OSS, Gemma3, Llama3.1, LCG decreases language confusion significantly, often by an order of magnitude, without negatively impacting task performance. Code is available at https://github.com/collinzrj/language_confusion_gate.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17318v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>CausalMamba：用于神经因果推断的可扩展条件状态空间模型
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
        </span>
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        <div class="mb-2 text-base text-gray-700">
            CausalMamba: Scalable Conditional State Space Models for Neural Causal Inference
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sangyoon Bae, Jiook Cha
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究从fMRI数据推断神经因果关系的逆问题，核心方法是采用两阶段框架：BOLD信号反卷积恢复潜在神经活动，再通过条件Mamba架构进行因果图推断。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于神经科学领域的因果推断，虽然方法上使用条件状态空间模型，但与推荐系统、搜索或广告的核心领域关联较弱。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 09:04:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17318v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17318v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce CausalMamba, a scalable framework that addresses fundamental limitations in fMRI-based causal inference: the ill-posed nature of inferring neural causality from hemodynamically distorted BOLD signals and the computational intractability of existing methods like Dynamic Causal Modeling (DCM). Our approach decomposes this complex inverse problem into two tractable stages: BOLD deconvolution to recover latent neural activity, followed by causal graph inference using a novel Conditional Mamba architecture. On simulated data, CausalMamba achieves 37% higher accuracy than DCM. Critically, when applied to real task fMRI data, our method recovers well-established neural pathways with 88% fidelity, whereas conventional approaches fail to identify these canonical circuits in over 99% of subjects. Furthermore, our network analysis of working memory data reveals that the brain strategically shifts its primary causal hub-recruiting executive or salience networks depending on the stimulus-a sophisticated reconfiguration that remains undetected by traditional methods. This work provides neuroscientists with a practical tool for large-scale causal inference that captures both fundamental circuit motifs and flexible network dynamics underlying cognitive function.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17218v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>当单时刻不足时：基于跨时刻交互的多时刻检索
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            When One Moment Isn't Enough: Multi-Moment Retrieval with Cross-Moment Interactions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhuo Cao, Heming Du, Bingqing Zhang, Xin Yu, Xue Li, Sen Wang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究视频中一个查询对应多个相关时刻的检索问题，核心方法是提出多时刻后验证模块，通过约束时间调整和验证机制来优化时刻边界并实现鲁棒的多时刻对齐。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频时序定位中的多时刻检索问题，虽然提出了新的数据集和边界优化方法，但与推荐系统、搜索广告的核心技术关联度较低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 07:01:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17218v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17218v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing Moment retrieval (MR) methods focus on Single-Moment Retrieval (SMR). However, one query can correspond to multiple relevant moments in real-world applications. This makes the existing datasets and methods insufficient for video temporal grounding. By revisiting the gap between current MR tasks and real-world applications, we introduce a high-quality datasets called QVHighlights Multi-Moment Dataset (QV-M$^2$), along with new evaluation metrics tailored for multi-moment retrieval (MMR). QV-M$^2$ consists of 2,212 annotations covering 6,384 video segments. Building on existing efforts in MMR, we propose a framework called FlashMMR. Specifically, we propose a Multi-moment Post-verification module to refine the moment boundaries. We introduce constrained temporal adjustment and subsequently leverage a verification module to re-evaluate the candidate segments. Through this sophisticated filtering pipeline, low-confidence proposals are pruned, and robust multi-moment alignment is achieved. We retrain and evaluate 6 existing MR methods on QV-M$^2$ and QVHighlights under both SMR and MMR settings. Results show that QV-M$^2$ serves as an effective benchmark for training and evaluating MMR models, while FlashMMR provides a strong baseline. Specifically, on QV-M$^2$, it achieves improvements over prior SOTA method by 3.00% on G-mAP, 2.70% on mAP@3+tgt, and 2.56% on mR@3. The proposed benchmark and method establish a foundation for advancing research in more realistic and challenging video temporal grounding scenarios. Code is released at https://github.com/Zhuo-Cao/QV-M2.
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            <a href="https://www.alphaxiv.org/abs/2510.17700v1" target="_blank" rel="noopener noreferrer">
                无需重新训练即可从预训练模型构建弹性视觉Transformer
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            <i class="fa fa-star mr-1"></i>7/10
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        <div class="mb-2 text-base text-gray-700">
            Elastic ViTs from Pretrained Models without Retraining
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Walter Simoncini, Michael Dorkenwald, Tijmen Blankevoort, Cees G. M. Snoek, Yuki...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文属于'使能Transformer技术'范畴，专注于ViT架构的效率改进。弹性ViT技术可以通过动态调整模型复杂度来优化推荐系统中的推理效率，在保持性能的同时降低计算成本，这对于大规模推荐和广告系统具有直接应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:15:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17700v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17700v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Vision foundation models achieve remarkable performance but are only available in a limited set of pre-determined sizes, forcing sub-optimal deployment choices under real-world constraints. We introduce SnapViT: Single-shot network approximation for pruned Vision Transformers, a new post-pretraining structured pruning method that enables elastic inference across a continuum of compute budgets. Our approach efficiently combines gradient information with cross-network structure correlations, approximated via an evolutionary algorithm, does not require labeled data, generalizes to models without a classification head, and is retraining-free. Experiments on DINO, SigLIPv2, DeIT, and AugReg models demonstrate superior performance over state-of-the-art methods across various sparsities, requiring less than five minutes on a single A100 GPU to generate elastic models that can be adjusted to any computational budget. Our key contributions include an efficient pruning strategy for pretrained Vision Transformers, a novel evolutionary approximation of Hessian off-diagonal structures, and a self-supervised importance scoring mechanism that maintains strong performance without requiring retraining or labels. Code and pruned models are available at: https://elastic.ashita.nl/
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            <a href="https://www.alphaxiv.org/abs/2510.17394v1" target="_blank" rel="noopener noreferrer">
                MILES：基于模态信息的用于平衡多模态学习的学习率调度器
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            <i class="fa fa-star mr-1"></i>7/10
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            MILES: Modality-Informed Learning Rate Scheduler for Balancing Multimodal Learning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Alejandro Guerra-Manzanares, Farah E. Shamout
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的模态感知学习率调度器技术属于Transformer效率优化范畴，可视为一种使能技术。在推荐系统和搜索场景中，处理用户行为序列、上下文特征等多模态数据时，这种平衡学习机制可显著提升模型对异构数据的融合能力，类似于VLM中处理不同模态的方法。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:34:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17394v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17394v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    The aim of multimodal neural networks is to combine diverse data sources, referred to as modalities, to achieve enhanced performance compared to relying on a single modality. However, training of multimodal networks is typically hindered by modality overfitting, where the network relies excessively on one of the available modalities. This often yields sub-optimal performance, hindering the potential of multimodal learning and resulting in marginal improvements relative to unimodal models. In this work, we present the Modality-Informed Learning ratE Scheduler (MILES) for training multimodal joint fusion models in a balanced manner. MILES leverages the differences in modality-wise conditional utilization rates during training to effectively balance multimodal learning. The learning rate is dynamically adjusted during training to balance the speed of learning from each modality by the multimodal model, aiming for enhanced performance in both multimodal and unimodal predictions. We extensively evaluate MILES on four multimodal joint fusion tasks and compare its performance to seven state-of-the-art baselines. Our results show that MILES outperforms all baselines across all tasks and fusion methods considered in our study, effectively balancing modality usage during training. This results in improved multimodal performance and stronger modality encoders, which can be leveraged when dealing with unimodal samples or absent modalities. Overall, our work highlights the impact of balancing multimodal learning on improving model performance.
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            <a href="https://www.alphaxiv.org/abs/2510.17205v1" target="_blank" rel="noopener noreferrer">
                视觉剪枝器：解码非连续跨模态动态以实现高效多模态大语言模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
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        <div class="mb-2 text-base text-gray-700">
            $\mathcal{V}isi\mathcal{P}runer$: Decoding Discontinuous Cross-Modal Dynamics for Efficient Multimodal LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yingqi Fan, Anhao Zhao, Jinlan Fu, Junlong Tong, Hui Su, Yijie Pan, Wei Zhang, X...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于多模态LLM的效率优化，属于'使能LLM技术'范畴，通过解码跨模态动态来提升模型效率。这种效率优化技术可应用于搜索和推荐系统中的多模态内容处理，例如处理图像-文本混合内容时减少计算开销，从而提升大规模推荐和搜索系统的性能。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 06:40:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17205v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17205v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal Large Language Models (MLLMs) have achieved strong performance across vision-language tasks, but suffer from significant computational overhead due to the quadratic growth of attention computations with the number of multimodal tokens. Though efforts have been made to prune tokens in MLLMs, \textit{they lack a fundamental understanding of how MLLMs process and fuse multimodal information.} Through systematic analysis, we uncover a \textbf{three-stage} cross-modal interaction process: (1) Shallow layers recognize task intent, with visual tokens acting as passive attention sinks; (2) Cross-modal fusion occurs abruptly in middle layers, driven by a few critical visual tokens; (3) Deep layers discard vision tokens, focusing solely on linguistic refinement. Based on these findings, we propose \emph{VisiPruner}, a training-free pruning framework that reduces up to 99\% of vision-related attention computations and 53.9\% of FLOPs on LLaVA-v1.5 7B. It significantly outperforms existing token pruning methods and generalizes across diverse MLLMs. Beyond pruning, our insights further provide actionable guidelines for training efficient MLLMs by aligning model architecture with its intrinsic layer-wise processing dynamics. Our code is available at: https://github.com/EIT-NLP/VisiPruner.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17685v1" target="_blank" rel="noopener noreferrer">
                通过双向关系推理与对齐实现多语言文本到图像的人物检索
            </a>
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        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Multilingual Text-to-Image Person Retrieval via Bidirectional Relation Reasoning and Aligning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Min Cao, Xinyu Zhou, Ding Jiang, Bo Du, Mang Ye, Min Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及跨模态检索技术，与搜索系统中的多模态搜索直接相关。双向关系推理和对齐方法可以应用于推荐系统中处理异构数据（如用户行为序列和内容特征），类似于VLM处理不同模态的思路。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:01:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17685v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17685v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Text-to-image person retrieval (TIPR) aims to identify the target person using textual descriptions, facing challenge in modality heterogeneity. Prior works have attempted to address it by developing cross-modal global or local alignment strategies. However, global methods typically overlook fine-grained cross-modal differences, whereas local methods require prior information to explore explicit part alignments. Additionally, current methods are English-centric, restricting their application in multilingual contexts. To alleviate these issues, we pioneer a multilingual TIPR task by developing a multilingual TIPR benchmark, for which we leverage large language models for initial translations and refine them by integrating domain-specific knowledge. Correspondingly, we propose Bi-IRRA: a Bidirectional Implicit Relation Reasoning and Aligning framework to learn alignment across languages and modalities. Within Bi-IRRA, a bidirectional implicit relation reasoning module enables bidirectional prediction of masked image and text, implicitly enhancing the modeling of local relations across languages and modalities, a multi-dimensional global alignment module is integrated to bridge the modality heterogeneity. The proposed method achieves new state-of-the-art results on all multilingual TIPR datasets. Data and code are presented in https://github.com/Flame-Chasers/Bi-IRRA.
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            <a href="https://www.alphaxiv.org/abs/2510.17197v1" target="_blank" rel="noopener noreferrer">
                ZSPAPrune：面向视觉语言模型的零样本提示感知令牌剪枝
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            <i class="fa fa-star mr-1"></i>6/10
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        <div class="mb-2 text-base text-gray-700">
            ZSPAPrune: Zero-Shot Prompt-Aware Token Pruning for Vision-Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pu Zhang, Yuwei Li, Xingyuan Xian, Guoming Tang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的零样本提示感知令牌剪枝技术属于Transformer效率优化范畴，直接关联'Enabling Transformer Tech'焦点。这种令牌剪枝方法可应用于推荐和搜索系统中的多模态内容理解，通过减少计算开销来提升大规模VLM在商品检索和内容匹配中的推理效率。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 06:18:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17197v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17197v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    As the capabilities of Vision-Language Models (VLMs) advance, they can process increasingly large inputs, which, unlike in LLMs, generates significant visual token redundancy and leads to prohibitive inference costs. While many methods aim to reduce these costs by pruning visual tokens, existing approaches, whether based on attention or diversity, typically neglect the guidance of the text prompt and thus fail to prioritize task relevance. In this work, we propose a novel, zero-shot method that reframes the problem by introducing a prompt-aware perspective, explicitly modeling visual token pruning as a balance between task relevance and information diversity. Our hierarchical approach first selects a core set of task-relevant visual tokens and then supplements them with diversity tokens to preserve broader context. Experiments across multiple models and benchmarks show that our method achieves performance that matches or surpasses the state-of-the-art with only minimal accuracy loss, even when pruning up to 90\% of the tokens. Furthermore, these gains are accompanied by significant reductions in GPU memory footprint and inference latency.
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            <a href="https://www.alphaxiv.org/abs/2510.17171v1" target="_blank" rel="noopener noreferrer">
                生成后重建：通过两阶段采样加速掩码自回归模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Generation then Reconstruction: Accelerating Masked Autoregressive Models via Two-Stage Sampling
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Feihong Yan, Peiru Wang, Yao Zhu, Kaiyu Pang, Qingyan Wei, Huiqi Li, Linfeng Zha...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种加速掩码自回归模型的两阶段采样方法，属于Transformer架构效率改进的范畴。这种加速技术可以应用于推荐系统和搜索中的序列建模任务，例如通过更高效的生成式推荐或搜索查询补全来提升系统性能。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 05:22:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17171v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17171v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Masked Autoregressive (MAR) models promise better efficiency in visual generation than autoregressive (AR) models for the ability of parallel generation, yet their acceleration potential remains constrained by the modeling complexity of spatially correlated visual tokens in a single step. To address this limitation, we introduce Generation then Reconstruction (GtR), a training-free hierarchical sampling strategy that decomposes generation into two stages: structure generation establishing global semantic scaffolding, followed by detail reconstruction efficiently completing remaining tokens. Assuming that it is more difficult to create an image from scratch than to complement images based on a basic image framework, GtR is designed to achieve acceleration by computing the reconstruction stage quickly while maintaining the generation quality by computing the generation stage slowly. Moreover, observing that tokens on the details of an image often carry more semantic information than tokens in the salient regions, we further propose Frequency-Weighted Token Selection (FTS) to offer more computation budget to tokens on image details, which are localized based on the energy of high frequency information. Extensive experiments on ImageNet class-conditional and text-to-image generation demonstrate 3.72x speedup on MAR-H while maintaining comparable quality (e.g., FID: 1.59, IS: 304.4 vs. original 1.59, 299.1), substantially outperforming existing acceleration methods across various model scales and generation tasks. Our codes will be released in https://github.com/feihongyan1/GtR.
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            <a href="https://www.alphaxiv.org/abs/2510.17620v1" target="_blank" rel="noopener noreferrer">
                遗忘以知，记忆以用：面向大语言模型的上下文感知遗忘
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            <i class="fa fa-star mr-1"></i>4/10
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        <div class="mb-2 text-base text-gray-700">
            Forget to Know, Remember to Use: Context-Aware Unlearning for Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuefeng Peng, Parnian Afshar, Megan Ganji, Thomas Butler, Amir Houmansadr, Mingx...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注LLM的遗忘机制，属于核心LLM技术进展，在推荐/搜索系统中可用于动态更新模型知识或处理用户偏好变化。然而，论文主要聚焦遗忘技术本身，而非其在推荐/搜索/广告中的直接应用，因此相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:03:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17620v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17620v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models may encode sensitive information or outdated knowledge that needs to be removed, to ensure responsible and compliant model responses. Unlearning has emerged as an efficient alternative to full retraining, aiming to remove specific knowledge while preserving overall model utility. Existing evaluations of unlearning methods focus on (1) the extent of forgetting of the target knowledge (forget set) and (2) maintaining performance on the retain set (i.e., utility). However, these evaluations overlook an important usability aspect: users may still want the model to leverage the removed information if it is re-introduced in the prompt. In a systematic evaluation of six state-of-the-art unlearning methods, we find that they consistently impair such contextual utility. To address this, we augment unlearning objectives with a plug-in term that preserves the model's ability to use forgotten knowledge when it is present in context. Extensive experiments demonstrate that our approach restores contextual utility to near original levels while still maintaining effective forgetting and retain-set utility.
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            <a href="https://www.alphaxiv.org/abs/2510.17795v1" target="_blank" rel="noopener noreferrer">
                用于复现人工智能研究的可执行知识图谱
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Executable Knowledge Graphs for Replicating AI Research
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注AI研究的复现性，这属于研究基础设施和可复现性范畴，与推荐系统、搜索或广告的核心技术进展没有直接关联。虽然知识图谱技术本身在推荐和搜索中有应用，但论文聚焦于研究复现而非具体的推荐/搜索算法改进，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:53:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17795v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17795v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.MA</span><span class="category-tag">cs.SE</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Replicating AI research is a crucial yet challenging task for large language model (LLM) agents. Existing approaches often struggle to generate executable code, primarily due to insufficient background knowledge and the limitations of retrieval-augmented generation (RAG) methods, which fail to capture latent technical details hidden in referenced papers. Furthermore, previous approaches tend to overlook valuable implementation-level code signals and lack structured knowledge representations that support multi-granular retrieval and reuse. To overcome these challenges, we propose Executable Knowledge Graphs (xKG), a modular and pluggable knowledge base that automatically integrates technical insights, code snippets, and domain-specific knowledge extracted from scientific literature. When integrated into three agent frameworks with two different LLMs, xKG shows substantial performance gains (10.9% with o3-mini) on PaperBench, demonstrating its effectiveness as a general and extensible solution for automated AI research replication. Code will released at https://github.com/zjunlp/xKG.
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            <a href="https://www.alphaxiv.org/abs/2510.17548v1" target="_blank" rel="noopener noreferrer">
                当标注者意见分歧时，拓扑学提供解释：Mapper——一种用于探索文本嵌入几何与歧义性的拓扑工具
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            <i class="fa fa-star mr-1"></i>3/10
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            When Annotators Disagree, Topology Explains: Mapper, a Topological Tool for Exploring Text Embedding Geometry and Ambiguity
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nisrine Rair, Alban Goupil, Valeriu Vrabie, Emmanuel Chochoy
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文本嵌入空间的拓扑分析和标注歧义性，属于基础NLP研究。虽然文本嵌入在搜索和推荐中有应用，但论文焦点在于几何分析和标注分歧，而非直接面向RecSys/Search/Ads的算法改进。拓扑工具Mapper可能有助于理解嵌入空间结构，从而间接改进语义匹配，但应用路径不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 13:58:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17548v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17548v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Language models are often evaluated with scalar metrics like accuracy, but such measures fail to capture how models internally represent ambiguity, especially when human annotators disagree. We propose a topological perspective to analyze how fine-tuned models encode ambiguity and more generally instances. Applied to RoBERTa-Large on the MD-Offense dataset, Mapper, a tool from topological data analysis, reveals that fine-tuning restructures embedding space into modular, non-convex regions aligned with model predictions, even for highly ambiguous cases. Over $98\%$ of connected components exhibit $\geq 90\%$ prediction purity, yet alignment with ground-truth labels drops in ambiguous data, surfacing a hidden tension between structural confidence and label uncertainty. Unlike traditional tools such as PCA or UMAP, Mapper captures this geometry directly uncovering decision regions, boundary collapses, and overconfident clusters. Our findings position Mapper as a powerful diagnostic tool for understanding how models resolve ambiguity. Beyond visualization, it also enables topological metrics that may inform proactive modeling strategies in subjective NLP tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.17431v1" target="_blank" rel="noopener noreferrer">
                面向搜索的智能体强化学习存在安全隐患
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            <i class="fa fa-star mr-1"></i>3/10
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            Agentic Reinforcement Learning for Search is Unsafe
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yushi Yang, Shreyansh Padarha, Andrew Lee, Adam Mahdi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然论文涉及搜索领域的强化学习应用，但标题明确聚焦于安全性问题，这属于被排除的非技术性话题（安全、隐私、公平性等）。该论文可能讨论RL在搜索中的风险而非核心算法改进或实际应用，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 11:19:37
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                <a href="https://arxiv.org/abs/2510.17431v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17431v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Agentic reinforcement learning (RL) trains large language models to autonomously call tools during reasoning, with search as the most common application. These models excel at multi-step reasoning tasks, but their safety properties are not well understood. In this study, we show that RL-trained search models inherit refusal from instruction tuning and often deflect harmful requests by turning them into safe queries. However, this safety is fragile. Two simple attacks, one that forces the model to begin response with search (Search attack), another that encourages models to repeatedly search (Multi-search attack), trigger cascades of harmful searches and answers. Across two model families (Qwen, Llama) with both local and web search, these attacks lower refusal rates by up to 60.0%, answer safety by 82.5%, and search-query safety by 82.4%. The attacks succeed by triggering models to generate harmful, request-mirroring search queries before they can generate the inherited refusal tokens. This exposes a core weakness of current RL training: it rewards continued generation of effective queries without accounting for their harmfulness. As a result, RL search models have vulnerabilities that users can easily exploit, making it urgent to develop safety-aware agentic RL pipelines optimising for safe search.
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            <a href="https://www.alphaxiv.org/abs/2510.17388v1" target="_blank" rel="noopener noreferrer">
                原子指令差距：指令调优大语言模型在处理简单、自包含的指令时表现不佳
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            The Atomic Instruction Gap: Instruction-Tuned LLMs Struggle with Simple, Self-Contained Directives
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Henry Lim, Kwan Hui Lim
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM在指令遵循能力方面的局限性，属于核心LLM技术的基础研究。虽然指令遵循能力对于RecSys/Search/Ads中的用户意图理解有一定潜在应用价值，但论文焦点更偏向于纯粹的NLP能力评估而非具体的应用场景，与当前关注点的直接关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:26:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17388v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17388v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Instruction-tuned large language models (IT-LLMs) exhibit strong zero-shot reasoning, yet their ability to execute simple, self-contained instructions remains underexplored, despite this being foundational to complex instruction-following. We evaluate 20 IT-LLMs on modified MMLU and MMLU-Pro benchmarks, by systematically varying the format of option labels (alphabetic, numeric, Roman) while keeping their meaning identical under four paradigms, namely: (1) With explicit instructions, label changes cause large performance shifts (e.g., -30.45\% for Roman vs. numeric), revealing instruction-format bias. (2) Without instructions, performance drops further (up to -10.84\%) and label sensitivity intensifies, underscoring the role of explicit guidance. (3) When option contents are removed, models fail random-choice baselines except with numeric labels, suggesting weak adherence to atomic directives. (4) Three-shot exemplars yield no significant gains in robustness or fidelity, and generation analyses show persistent label errors, especially for non-numeric formats. Across model sizes, larger LLMs achieve higher accuracy but remain inconsistent in instruction adherence. These results expose the insufficiencies of current instruction-tuning paradigms and highlight the need for evaluation methods and training strategies that explicitly target atomic instruction-following.
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            <a href="https://www.alphaxiv.org/abs/2510.17777v1" target="_blank" rel="noopener noreferrer">
                SparseVILA：解耦视觉稀疏性以实现高效视觉语言模型推理
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            SparseVILA: Decoupling Visual Sparsity for Efficient VLM Inference
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Samir Khaki, Junxian Guo, Jiaming Tang, Shang Yang, Yukang Chen, Konstantinos N....
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型(VLM)的推理效率优化，虽然涉及Transformer架构的效率改进，但其核心应用场景是视觉-语言任务。对于推荐系统、搜索或广告领域，这种视觉稀疏性解耦技术的直接应用潜力有限，除非系统需要处理大量视觉内容的多模态推荐或搜索场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:35:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17777v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17777v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Vision Language Models (VLMs) have rapidly advanced in integrating visual and textual reasoning, powering applications across high-resolution image understanding, long-video analysis, and multi-turn conversation. However, their scalability remains limited by the growing number of visual tokens that dominate inference latency. We present SparseVILA, a new paradigm for efficient VLM inference that decouples visual sparsity across the prefilling and decoding stages. SparseVILA distributes sparsity across stages by pruning redundant visual tokens during prefill and retrieving only query-relevant tokens during decoding. This decoupled design matches leading prefill pruning methods while preserving multi-turn fidelity by retaining most of the visual cache so that query-aware tokens can be retrieved at each conversation round. Built on an AWQ-optimized inference pipeline, SparseVILA achieves up to 4.0 times faster prefilling, 2.5 times faster decoding, and an overall 2.6 times end-to-end speedup on long-context video tasks -- while improving accuracy on document-understanding and reasoning tasks. By decoupling query-agnostic pruning and query-aware retrieval, SparseVILA establishes a new direction for efficient multimodal inference, offering a training-free, architecture-agnostic framework for accelerating large VLMs without sacrificing capability.
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            <a href="https://www.alphaxiv.org/abs/2510.17364v1" target="_blank" rel="noopener noreferrer">
                基于循环注意力的令牌选择用于高效流式视频大语言模型
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            Recurrent Attention-based Token Selection for Efficient Streaming Video-LLMs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Vaggelis Dorovatas, Soroush Seifi, Gunshi Gupta, Rahaf Aljundi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频-LLM的高效处理，属于视觉模态的优化，与RecSys/Search/Ads的核心关注点关联较弱。虽然注意力机制和效率优化是Transformer技术的一部分，但论文聚焦于视频流处理，缺乏明确的推荐、搜索或广告应用场景的直接相关性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:04:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17364v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17364v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Video Large Language Models (Video-LLMs) excel at understanding videos in-context, provided they have full access to the video when answering queries. However, these models face challenges in streaming scenarios where hour-long videos must be processed online, and questions need timely responses. In this work, we propose a training-free approach compatible with standard Video-LLMs, leveraging three key concepts: 1) LLM-informed selection of visual tokens to identify those that the LLM has attended to and contributed to its understanding of each short clip. Our attention-based selection allows us to discard up to ~95% of unimportant visual tokens with minimal performance loss; 2) Recurrent processing of past selected tokens to generate temporally coherent understanding of each processed clip; 3) Caption-based question answering for lightweight and accurate responses. Our method achieves state-of-the-art performance on streaming video benchmarks, striking a balance between efficiency and effectiveness.
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            <a href="https://www.alphaxiv.org/abs/2510.17274v1" target="_blank" rel="noopener noreferrer">
                基于即插即用多模态大语言模型的增强运动预测
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            Enhanced Motion Forecasting with Plug-and-Play Multimodal Large Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Katie Luo, Jingwei Ji, Tong He, Runsheng Xu, Yichen Xie, Dragomir Anguelov, Ming...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注运动预测，属于计算机视觉和自动驾驶领域，与推荐系统、搜索或广告的核心领域没有直接关联。虽然涉及多模态大语言模型技术，但其在RecSys/Search/Ads中的潜在应用不明确，因为运动预测主要处理物理轨迹数据而非用户行为序列或内容理解。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 08:01:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17274v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17274v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Current autonomous driving systems rely on specialized models for perceiving and predicting motion, which demonstrate reliable performance in standard conditions. However, generalizing cost-effectively to diverse real-world scenarios remains a significant challenge. To address this, we propose Plug-and-Forecast (PnF), a plug-and-play approach that augments existing motion forecasting models with multimodal large language models (MLLMs). PnF builds on the insight that natural language provides a more effective way to describe and handle complex scenarios, enabling quick adaptation to targeted behaviors. We design prompts to extract structured scene understanding from MLLMs and distill this information into learnable embeddings to augment existing behavior prediction models. Our method leverages the zero-shot reasoning capabilities of MLLMs to achieve significant improvements in motion prediction performance, while requiring no fine-tuning -- making it practical to adopt. We validate our approach on two state-of-the-art motion forecasting models using the Waymo Open Motion Dataset and the nuScenes Dataset, demonstrating consistent performance improvements across both benchmarks.
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            <a href="https://www.alphaxiv.org/abs/2510.17670v1" target="_blank" rel="noopener noreferrer">
                基于FLAME的实时开放词汇目标检测自适应：通过主动边缘样本探索实现少样本定位
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            On-the-Fly OVD Adaptation with FLAME: Few-shot Localization via Active Marginal-Samples Exploration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yehonathan Refael, Amit Aides, Aviad Barzilai, George Leifman, Genady Beryozkin,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的开放词汇目标检测和少样本学习，虽然标题提到'实时自适应'技术，但其核心是视觉定位问题。对于推荐系统、搜索或广告领域，这种视觉定位技术的潜在应用非常有限且间接，主要可能用于图像内容理解，但缺乏明确的推荐/搜索/广告应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:41:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17670v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17670v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Open-vocabulary object detection (OVD) models offer remarkable flexibility by detecting objects from arbitrary text queries. However, their zero-shot performance in specialized domains like Remote Sensing (RS) is often compromised by the inherent ambiguity of natural language, limiting critical downstream applications. For instance, an OVD model may struggle to distinguish between fine-grained classes such as "fishing boat" and "yacht" since their embeddings are similar and often inseparable. This can hamper specific user goals, such as monitoring illegal fishing, by producing irrelevant detections. To address this, we propose a cascaded approach that couples the broad generalization of a large pre-trained OVD model with a lightweight few-shot classifier. Our method first employs the zero-shot model to generate high-recall object proposals. These proposals are then refined for high precision by a compact classifier trained in real-time on only a handful of user-annotated examples - drastically reducing the high costs of RS imagery annotation.The core of our framework is FLAME, a one-step active learning strategy that selects the most informative samples for training. FLAME identifies, on the fly, uncertain marginal candidates near the decision boundary using density estimation, followed by clustering to ensure sample diversity. This efficient sampling technique achieves high accuracy without costly full-model fine-tuning and enables instant adaptation, within less then a minute, which is significantly faster than state-of-the-art alternatives.Our method consistently surpasses state-of-the-art performance on RS benchmarks, establishing a practical and resource-efficient framework for adapting foundation models to specific user needs.
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            <a href="https://www.alphaxiv.org/abs/2510.17281v1" target="_blank" rel="noopener noreferrer">
                MemoryBench：面向LLM系统的记忆与持续学习基准
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            <i class="fa fa-star mr-1"></i>2/10
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            MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qingyao Ai, Yichen Tang, Changyue Wang, Jianming Long, Weihang Su, Yiqun Liu
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的记忆和持续学习基准测试，属于纯粹的评估基准范畴，这在无关主题中明确排除。虽然持续学习技术本身可能对推荐系统有价值，但作为基准论文缺乏对RecSys/Search/Ads的直接应用潜力，且基准评估属于被排除的NLP中心话题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 08:16:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17281v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17281v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span></div>
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                    Scaling up data, parameters, and test-time computation has been the mainstream methods to improve LLM systems (LLMsys), but their upper bounds are almost reached due to the gradual depletion of high-quality data and marginal gains obtained from larger computational resource consumption. Inspired by the abilities of human and traditional AI systems in learning from practice, constructing memory and continual learning frameworks for LLMsys has become an important and popular research direction in recent literature. Yet, existing benchmarks for LLM memory often focus on evaluating the system on homogeneous reading comprehension tasks with long-form inputs rather than testing their abilities to learn from accumulated user feedback in service time. Therefore, we propose a user feedback simulation framework and a comprehensive benchmark covering multiple domains, languages, and types of tasks to evaluate the continual learning abilities of LLMsys. Experiments show that the effectiveness and efficiency of state-of-the-art baselines are far from satisfying, and we hope this benchmark could pave the way for future studies on LLM memory and optimization algorithms.
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            <a href="https://www.alphaxiv.org/abs/2510.17228v1" target="_blank" rel="noopener noreferrer">
                DSEBench：基于示例的可解释数据集搜索测试集
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            <i class="fa fa-star mr-1"></i>2/10
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            DSEBench: A Test Collection for Explainable Dataset Search with Examples
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qing Shi, Jing He, Qiaosheng Chen, Gong Cheng
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注数据集搜索的测试基准和可解释性，属于数据管理领域而非推荐系统、搜索或广告的核心技术。虽然数据集搜索在概念上与信息检索相关，但该工作更偏向数据发现和基准评估，与当前关注的LLM技术、Transformer架构进展或异构数据统一建模没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 07:19:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17228v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17228v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span></div>
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                    Dataset search has been an established information retrieval task. Current paradigms either retrieve datasets that are relevant to a keyword query or find datasets that are similar to an input target dataset. To allow for their combined specification of information needs, in this article, we investigate the more generalized task of Dataset Search with Examples (DSE) and further extend it to Explainable DSE that requires identifying the metadata and content fields of a dataset that indicate its relevance to the query and similarity to the target datasets. To facilitate this research, we construct DSEBench, a test collection that provides high-quality dataset- and field-level annotations to enable the evaluation of explainable DSE. We also employ a large language model to generate numerous annotations to be used for training. We establish extensive baselines on DSEBench by adapting and evaluating a variety of sparse, dense, and LLM-based retrieval, reranking, and explanation methods.
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            <a href="https://www.alphaxiv.org/abs/2510.17797v1" target="_blank" rel="noopener noreferrer">
                企业深度研究：面向企业分析的可控多智能体深度研究
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            Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Akshara Prabhakar, Roshan Ram, Zixiang Chen, Silvio Savarese, Frank Wang, Caimin...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于企业分析和多智能体系统，与推荐系统、搜索或广告的核心领域关联较弱。虽然多智能体系统可能有潜在的分布式决策应用，但标题未明确涉及LLM技术、Transformer架构或推荐/搜索/广告的具体应用场景，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:55:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17797v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17797v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As information grows exponentially, enterprises face increasing pressure to transform unstructured data into coherent, actionable insights. While autonomous agents show promise, they often struggle with domain-specific nuances, intent alignment, and enterprise integration. We present Enterprise Deep Research (EDR), a multi-agent system that integrates (1) a Master Planning Agent for adaptive query decomposition, (2) four specialized search agents (General, Academic, GitHub, LinkedIn), (3) an extensible MCP-based tool ecosystem supporting NL2SQL, file analysis, and enterprise workflows, (4) a Visualization Agent for data-driven insights, and (5) a reflection mechanism that detects knowledge gaps and updates research direction with optional human-in-the-loop steering guidance. These components enable automated report generation, real-time streaming, and seamless enterprise deployment, as validated on internal datasets. On open-ended benchmarks including DeepResearch Bench and DeepConsult, EDR outperforms state-of-the-art agentic systems without any human steering. We release the EDR framework and benchmark trajectories to advance research on multi-agent reasoning applications. Code at https://github.com/SalesforceAIResearch/enterprise-deep-research and Dataset at https://huggingface.co/datasets/Salesforce/EDR-200
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            <a href="https://www.alphaxiv.org/abs/2510.17793v1" target="_blank" rel="noopener noreferrer">
                基础自动评估器：面向推理中心领域的多任务生成式评估器训练的规模化
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            Foundational Automatic Evaluators: Scaling Multi-Task Generative Evaluator Training for Reasoning-Centric Domains
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Austin Xu, Xuan-Phi Nguyen, Yilun Zhou, Chien-Sheng Wu, Caiming Xiong, Shafiq Jo...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注生成式评估器的自动评估和训练方法，属于LLM评估和基准测试范畴，这在您的关注点中被明确列为无关主题。虽然评估技术可能间接影响推荐或搜索系统的开发，但论文标题强调推理中心领域的评估器训练，缺乏与RecSys/Search/Ads的直接关联或明确的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:52:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17793v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17793v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Finetuning specialized generative evaluators has emerged as a popular paradigm to meet the increasing demand for scalable evaluation during both training and test-time. However, recent work has largely focused on applying new methodology, such as reinforcement learning (RL), to training evaluators, shying away from large-scale, data-driven development. In this work, we focus on data scaling, curating a set of 2.5M samples spanning five unique evaluation tasks (pairwise, step-level, reference-free and reference-based verification, and single rating) and multiple domains focused on reasoning evaluation. With our data, we train Foundational Automatic Reasoning Evaluators (FARE), a family of 8B and 20B (with 3.6B active) parameter evaluators, with a simple iterative rejection-sampling supervised finetuning (SFT) approach. FARE-8B challenges larger specialized RL-trained evaluators and FARE-20B sets the new standard for open-source evaluators, surpassing specialized 70B+ evaluators. Beyond static benchmarks, we evaluate FARE in real-world tasks: As inference-time rerankers, FARE-20B achieves near-oracle performance on MATH. As verifiers in RL training, FARE improves the downstream RL-trained model performance by up to 14.1% vs. string-matching verifiers. When initialized from FARE, a continually-finetuned FARE-Code outperforms gpt-oss-20B by 65% on evaluating test-case quality.
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            <a href="https://www.alphaxiv.org/abs/2510.17790v1" target="_blank" rel="noopener noreferrer">
                UltraCUA：具有混合动作的计算机使用智能体基础模型
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            UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuhao Yang, Zhen Yang, Zi-Yi Dou, Anh Nguyen, Keen You, Omar Attia, Andrew Szot,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机使用智能体和混合动作，属于通用AI智能体领域，与推荐系统、搜索或广告的核心技术关联较弱。虽然基础模型技术本身具有广泛适用性，但论文标题未表明在RecSys/Search/Ads领域的直接应用潜力，更多聚焦于人机交互和任务自动化场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:48:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17790v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17790v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Multimodal agents for computer use rely exclusively on primitive actions (click, type, scroll) that require accurate visual grounding and lengthy execution chains, leading to cascading failures and performance bottlenecks. While other agents leverage rich programmatic interfaces (APIs, MCP servers, tools), computer-use agents (CUAs) remain isolated from these capabilities. We present UltraCUA, a foundation model that bridges this gap through hybrid action -- seamlessly integrating GUI primitives with high-level programmatic tool calls. To achieve this, our approach comprises four key components: (1) an automated pipeline that scales programmatic tools from software documentation, open-source repositories, and code generation; (2) a synthetic data engine producing over 17,000 verifiable tasks spanning real-world computer-use scenarios; (3) a large-scale high-quality hybrid action trajectory collection with both low-level GUI actions and high-level programmatic tool calls; and (4) a two-stage training pipeline combining supervised fine-tuning with online reinforcement learning, enabling strategic alternation between low-level and high-level actions. Experiments with our 7B and 32B models demonstrate substantial improvements over state-of-the-art agents. On OSWorld, UltraCUA models achieve an average 22% relative improvement over base models, while being 11% faster in terms of steps. Out-of-domain evaluation on WindowsAgentArena shows our model reaches 21.7% success rate, outperforming baselines trained on Windows data. The hybrid action mechanism proves critical, reducing error propagation while maintaining execution efficiency.
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            <a href="https://www.alphaxiv.org/abs/2510.17776v1" target="_blank" rel="noopener noreferrer">
                大规模语言模型中的训练后遗忘映射
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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            Mapping Post-Training Forgetting in Language Models at Scale
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jackson Harmon, Andreas Hochlehnert, Matthias Bethge, Ameya Prabhu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究语言模型训练后的遗忘现象，属于模型稳定性与维护范畴，与推荐系统、搜索或广告的核心进展及直接应用关联较弱。虽然理解遗忘机制可能间接影响模型在动态环境中的持续学习，但缺乏明确的直接应用场景或技术突破点。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:35:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17776v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17776v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Scaled post-training now drives many of the largest capability gains in language models (LMs), yet its effect on pretrained knowledge remains poorly understood. Not all forgetting is equal: Forgetting one fact (e.g., a U.S. president or an API call) does not "average out" by recalling another. Hence, we propose a sample-wise paradigm to measure what is forgotten and when backward transfer occurs. Our metric counts 1->0 transitions (correct before post-training, incorrect after) to quantify forgetting and 0->1 transitions to quantify backward transfer. Traditional task averages conflate these effects and obscure large changes. For multiple-choice benchmarks, we add chance-adjusted variants that subtract the expected contribution of random guessing from pre- and post-training accuracies. We apply this framework across post-training stages, model sizes, and data scales. Our large-scale analysis shows that: (1) Domain-continual pretraining induces moderate forgetting with low-to-moderate backward transfer; (2) RL/SFT post-training applied to base models and Instruction tuning yields moderate-to-large backward transfer on math and logic with overall low-to-moderate forgetting; (3) Applying RL/SFT to instruction-tuned models is sensitive on data scale: at small scales, both forgetting and backward transfer are small; at larger scales, effects are mixed and warrant further study with better controls; (4) Model merging does not reliably mitigate forgetting. Overall, our framework offers a practical yardstick for mapping how post-training alters pretrained knowledge at scale -- enabling progress towards generally capable AI systems.
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            <a href="https://www.alphaxiv.org/abs/2510.17733v1" target="_blank" rel="noopener noreferrer">
                训练求真，保留技能：二元检索增强奖励缓解幻觉问题
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            Train for Truth, Keep the Skills: Binary Retrieval-Augmented Reward Mitigates Hallucinations
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tong Chen, Akari Asai, Luke Zettlemoyer, Hannaneh Hajishirzi, Faeze Brahman
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然论文涉及检索增强和幻觉缓解，但这主要属于纯粹的NLP评估基准和幻觉问题范畴，与我的关注点无关。检索增强技术本身可能有潜力应用于搜索系统，但论文标题明确聚焦于幻觉缓解这一纯粹的NLP问题，而非其在推荐系统或搜索中的实际应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:45:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17733v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17733v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Language models often generate factually incorrect information unsupported by their training data, a phenomenon known as extrinsic hallucination. Existing mitigation approaches often degrade performance on open-ended generation and downstream tasks, limiting their practical utility. We propose an online reinforcement learning method using a novel binary retrieval-augmented reward (RAR) to address this tradeoff. Unlike continuous reward schemes, our approach assigns a reward of one only when the model's output is entirely factually correct, and zero otherwise. We evaluate our method on Qwen3 reasoning models across diverse tasks. For open-ended generation, binary RAR achieves a 39.3% reduction in hallucination rates, substantially outperforming both supervised training and continuous-reward RL baselines. In short-form question answering, the model learns calibrated abstention, strategically outputting "I don't know" when faced with insufficient parametric knowledge. This yields 44.4% and 21.7% fewer incorrect answers on PopQA and GPQA, respectively. Crucially, these factuality gains come without performance degradation on instruction following, math, or code, whereas continuous-reward RL, despite improving factuality, induces quality regressions.
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            <a href="https://www.alphaxiv.org/abs/2510.17725v1" target="_blank" rel="noopener noreferrer">
                AcademicEval：实时长上下文大语言模型基准测试
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            AcademicEval: Live Long-Context LLM Benchmark
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haozhen Zhang, Tao Feng, Pengrui Han, Jiaxuan You
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注长上下文LLM的基准测试和评估，属于纯粹的NLP评估基准范畴。虽然长上下文处理能力对于某些推荐和搜索应用可能有间接价值，但论文本身专注于基准测试而非实际应用，属于明确的无关主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:42:30
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17725v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17725v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Large Language Models (LLMs) have recently achieved remarkable performance in long-context understanding. However, current long-context LLM benchmarks are limited by rigid context length, labor-intensive annotation, and the pressing challenge of label leakage issues during LLM training. Therefore, we propose \textsc{AcademicEval}, a live benchmark for evaluating LLMs over long-context generation tasks. \textsc{AcademicEval} adopts papers on arXiv to introduce several academic writing tasks with long-context inputs, \textit{i.e.}, \textsc{Title}, \textsc{Abstract}, \textsc{Introduction}, and \textsc{Related Work}, which cover a wide range of abstraction levels and require no manual labeling. Moreover, \textsc{AcademicEval} integrates high-quality and expert-curated few-shot demonstrations from a collected co-author graph to enable flexible context length. Especially, \textsc{AcademicEval} features an efficient live evaluation, ensuring no label leakage. We conduct a holistic evaluation on \textsc{AcademicEval}, and the results illustrate that LLMs perform poorly on tasks with hierarchical abstraction levels and tend to struggle with long few-shot demonstrations, highlighting the challenge of our benchmark. Through experimental analysis, we also reveal some insights for enhancing LLMs' long-context modeling capabilities. Code is available at https://github.com/ulab-uiuc/AcademicEval
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            <a href="https://www.alphaxiv.org/abs/2510.17720v1" target="_blank" rel="noopener noreferrer">
                PANER：一种用于低资源命名实体识别的释义增强框架
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            PANER: A Paraphrase-Augmented Framework for Low-Resource Named Entity Recognition
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nanda Kumar Rengarajan, Jun Yan, Chun Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于低资源命名实体识别这一特定NLP任务，属于传统信息抽取技术范畴。虽然命名实体识别在搜索系统中可用于查询理解和文档理解，但该论文主要解决数据稀缺问题而非核心推荐、搜索或广告系统的架构创新，与当前关注的LLM技术、Transformer架构进展或异构数据统一建模等焦点领域关联度较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:36:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17720v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17720v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Named Entity Recognition (NER) is a critical task that requires substantial annotated data, making it challenging in low-resource scenarios where label acquisition is expensive. While zero-shot and instruction-tuned approaches have made progress, they often fail to generalize to domain-specific entities and do not effectively utilize limited available data. We present a lightweight few-shot NER framework that addresses these challenges through two key innovations: (1) a new instruction tuning template with a simplified output format that combines principles from prior IT approaches to leverage the large context window of recent state-of-the-art LLMs; (2) introducing a strategic data augmentation technique that preserves entity information while paraphrasing the surrounding context, thereby expanding our training data without compromising semantic relationships. Experiments on benchmark datasets show that our method achieves performance comparable to state-of-the-art models on few-shot and zero-shot tasks, with our few-shot approach attaining an average F1 score of 80.1 on the CrossNER datasets. Models trained with our paraphrasing approach show consistent improvements in F1 scores of up to 17 points over baseline versions, offering a promising solution for groups with limited NER training data and compute power.
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            <a href="https://www.alphaxiv.org/abs/2510.17715v1" target="_blank" rel="noopener noreferrer">
                QueST：激励大型语言模型生成困难问题
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            QueST: Incentivizing LLMs to Generate Difficult Problems
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hanxu Hu, Xingxing Zhang, Jannis Vamvas, Rico Sennrich, Furu Wei
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的问题生成能力激励，属于纯粹的LLM能力优化范畴。虽然问题生成技术可能间接应用于搜索中的查询扩展或推荐中的内容理解，但论文本身并未直接涉及推荐系统、搜索或广告的核心技术，且缺乏明确的Transformer架构改进或异构数据建模等关键技术要素。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:29:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17715v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17715v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models have achieved strong performance on reasoning tasks, solving competition-level coding and math problems. However, their scalability is limited by human-labeled datasets and the lack of large-scale, challenging coding problem training data. Existing competitive coding datasets contain only thousands to tens of thousands of problems. Previous synthetic data generation methods rely on either augmenting existing instruction datasets or selecting challenging problems from human-labeled data. In this paper, we propose QueST, a novel framework which combines difficulty-aware graph sampling and difficulty-aware rejection fine-tuning that directly optimizes specialized generators to create challenging coding problems. Our trained generators demonstrate superior capability compared to even GPT-4o at creating challenging problems that benefit downstream performance. We leverage QueST to generate large-scale synthetic coding problems, which we then use to distill from strong teacher models with long chain-of-thought or to conduct reinforcement learning for smaller models, proving effective in both scenarios. Our distillation experiments demonstrate significant performance gains. Specifically, after fine-tuning Qwen3-8B-base on 100K difficult problems generated by QueST, we surpass the performance of the original Qwen3-8B on LiveCodeBench. With an additional 112K examples (i.e., 28K human-written problems paired with multiple synthetic solutions), our 8B model matches the performance of the much larger DeepSeek-R1-671B. These findings indicate that generating complex problems via QueST offers an effective and scalable approach to advancing the frontiers of competitive coding and reasoning for large language models.
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                从学习者-大语言模型教育对话中挖掘有效教学策略的研究
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            Towards Mining Effective Pedagogical Strategies from Learner-LLM Educational Dialogues
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Liqun He, Manolis Mavrikis, Mutlu Cukurova
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注教育领域的对话分析和教学策略挖掘，属于特定领域应用而非核心推荐系统、搜索或广告技术。虽然涉及LLM对话分析，但其教育应用场景与RecSys/Search/Ads领域缺乏直接关联，且未明确涉及推荐系统相关的用户建模、内容排序或广告投放等核心技术问题。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:11:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17698v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17698v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Dialogue plays a crucial role in educational settings, yet existing evaluation methods for educational applications of large language models (LLMs) primarily focus on technical performance or learning outcomes, often neglecting attention to learner-LLM interactions. To narrow this gap, this AIED Doctoral Consortium paper presents an ongoing study employing a dialogue analysis approach to identify effective pedagogical strategies from learner-LLM dialogues. The proposed approach involves dialogue data collection, dialogue act (DA) annotation, DA pattern mining, and predictive model building. Early insights are outlined as an initial step toward future research. The work underscores the need to evaluate LLM-based educational applications by focusing on dialogue dynamics and pedagogical strategies.
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            <a href="https://www.alphaxiv.org/abs/2510.17671v1" target="_blank" rel="noopener noreferrer">
                LILO：基于交互式自然语言反馈的贝叶斯优化
            </a>
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        <div class="mb-2 text-base text-gray-700">
            LILO: Bayesian Optimization with Interactive Natural Language Feedback
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Katarzyna Kobalczyk, Zhiyuan Jerry Lin, Benjamin Letham, Zhuokai Zhao, Maximilia...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注贝叶斯优化与自然语言反馈的结合，属于优化方法领域。虽然自然语言交互有一定相关性，但贝叶斯优化主要应用于超参数调优和实验设计，与推荐系统、搜索或广告的核心排序和建模问题关联较弱。该方法可能间接应用于系统参数优化，但缺乏对核心领域问题的直接针对性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:41:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17671v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17671v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    For many real-world applications, feedback is essential in translating complex, nuanced, or subjective goals into quantifiable optimization objectives. We propose a language-in-the-loop framework that uses a large language model (LLM) to convert unstructured feedback in the form of natural language into scalar utilities to conduct BO over a numeric search space. Unlike preferential BO, which only accepts restricted feedback formats and requires customized models for each domain-specific problem, our approach leverages LLMs to turn varied types of textual feedback into consistent utility signals and to easily include flexible user priors without manual kernel design. At the same time, our method maintains the sample efficiency and principled uncertainty quantification of BO. We show that this hybrid method not only provides a more natural interface to the decision maker but also outperforms conventional BO baselines and LLM-only optimizers, particularly in feedback-limited regimes.
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            <a href="https://www.alphaxiv.org/abs/2510.17652v1" target="_blank" rel="noopener noreferrer">
                Qomhra：一个双语爱尔兰语-英语大语言模型
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        <div class="mb-2 text-base text-gray-700">
            Qomhra: A Bilingual Irish-English Large Language Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Joseph McInerney
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注特定语言（爱尔兰语-英语）的双语LLM开发，这属于语言特定的模型构建工作。虽然LLM技术本身是相关领域，但该论文缺乏与推荐系统、搜索或广告领域的具体应用连接，也没有涉及Transformer架构改进或多模态建模等核心关注点。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:27:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17652v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17652v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">I.2.7</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper introduces Qomhr\'a, a bilingual Irish-English large language model (LLM), developed under low-resource constraints presenting a complete pipeline spanning bilingual continued pre-training, instruction tuning, and alignment from human preferences. Newly accessible Irish corpora and English text are mixed and curated to improve Irish performance while preserving English ability. 6 closed-weight LLMs are judged for their Irish text generation by a native speaker, a learner and other LLMs. Google's Gemini-2.5-Pro is ranked the highest and is subsequently used to synthesise instruction tuning and human preference datasets. Two datasets are contributed leveraging Gemini-2.5-Pro: a 30K Irish-English parallel instruction tuning dataset and a 1K human preference dataset, generating accepted and rejected responses that show near perfect alignment with a native Irish speaker. Qomhr\'a is comprehensively evaluated across benchmarks testing translation, gender understanding, topic identification and world knowledge with gains of up to 29% in Irish and 44% in English. Qomhr\'a also undergoes instruction tuning and demonstrates clear progress in instruction following, crucial for chatbot functionality.
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            <a href="https://www.alphaxiv.org/abs/2510.17598v1" target="_blank" rel="noopener noreferrer">
                用于改进代码生成的推理蒸馏与结构对齐
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            Reasoning Distillation and Structural Alignment for Improved Code Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Amir Jalilifard, Anderson de Rezende Rocha, Marcos Medeiros Raimundo
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于代码生成领域的推理蒸馏和结构对齐技术，属于纯粹的代码生成应用。虽然涉及蒸馏技术，但缺乏与推荐系统、搜索或广告领域的明确连接。代码生成本身属于AIGC范畴，属于明确排除的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:47:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17598v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17598v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Effective code generation with language models hinges on two critical factors: accurately understanding the intent of the prompt and generating code that applies algorithmic reasoning to produce correct solutions capable of passing diverse test cases while adhering to the syntax of the target programming language. Unlike other language tasks, code generation requires more than accurate token prediction; it demands comprehension of solution-level and structural relationships rather than merely generating the most likely tokens. very large language model (VLLM) are capable of generating detailed steps toward the correct solution of complex tasks where reasoning is crucial in solving the problem. Such reasoning capabilities may be absent in smaller language models. Therefore, in this work, we distill the reasoning capabilities of a VLLM into a smaller, more efficient model that is faster and cheaper to deploy. Our approach trains the model to emulate the reasoning and problem-solving abilities of the VLLM by learning to identify correct solution pathways and establishing a structural correspondence between problem definitions and potential solutions through a novel method of structure-aware loss optimization. This enables the model to transcend token-level generation and to deeply grasp the overarching structure of solutions for given problems. Experimental results show that our fine-tuned model, developed through a cheap and simple to implement process, significantly outperforms our baseline model in terms of pass@1, average data flow, and average syntax match metrics across the MBPP, MBPP Plus, and HumanEval benchmarks.
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            <a href="https://www.alphaxiv.org/abs/2510.17591v1" target="_blank" rel="noopener noreferrer">
                HGAdapter：基于超图的自适应模块在语言模型中的代码摘要与克隆检测应用
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            HGAdapter: Hypergraph-based Adapters in Language Models for Code Summarization and Clone Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guang Yang, Yujie Zhu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及语言模型架构改进（适配器设计），但其应用领域完全集中在代码理解和软件工程任务（代码摘要和克隆检测），与推荐系统、搜索或广告领域没有直接关联。超图适配器技术本身可能具有通用性，但论文没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:41:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17591v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17591v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.SE</span></div>
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                    Pre-trained language models (PLMs) are increasingly being applied to code-related tasks. Although PLMs have achieved good results, they do not take into account potential high-order data correlations within the code. We propose three types of high-order correlations in code tokens, i.e. abstract syntax tree family correlation, lexical correlation, and line correlation. We design a tokens and hyperedges generator to capture these high-order data correlations. We improve the architecture of hypergraph neural networks and combine it with adapter tuning to propose a novel hypergraph-based adapter (HGAdapter) to fine-tune PLMs. HGAdapter can encode high-order data correlations and is allowed to be inserted into various PLMs to enhance performance. Experiments were conducted on several public datasets, including six languages of code summarization and code clone detection tasks. Our methods improved the performance of PLMs in datasets to varying degrees. Experimental results validate the introduction of high-order data correlations that contribute to improved effectiveness.
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            <a href="https://www.alphaxiv.org/abs/2510.17590v1" target="_blank" rel="noopener noreferrer">
                MIRAGE：基于网络推理的多模态虚假信息检测智能体框架
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            MIRAGE: Agentic Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mir Nafis Sharear Shopnil, Sharad Duwal, Abhishek Tyagi, Adiba Mahbub Proma
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态虚假信息检测，这属于内容安全领域，而非推荐系统、搜索或广告的核心技术。虽然涉及多模态处理和网络推理，但这些技术在当前框架下主要应用于内容审核，与排名、推荐或广告相关性建模的直接应用关联较弱。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:40:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17590v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17590v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.CY</span><span class="category-tag">cs.LG</span><span class="category-tag">I.2.7; H.3.3; I.4.9</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Misinformation spreads across web platforms through billions of daily multimodal posts that combine text and images, overwhelming manual fact-checking capacity. Supervised detection models require domain-specific training data and fail to generalize across diverse manipulation tactics. We present MIRAGE, an inference-time, model-pluggable agentic framework that decomposes multimodal verification into four sequential modules: visual veracity assessment detects AI-generated images, cross-modal consistency analysis identifies out-of-context repurposing, retrieval-augmented factual checking grounds claims in web evidence through iterative question generation, and a calibrated judgment module integrates all signals. MIRAGE orchestrates vision-language model reasoning with targeted web retrieval, outputs structured and citation-linked rationales. On MMFakeBench validation set (1,000 samples), MIRAGE with GPT-4o-mini achieves 81.65% F1 and 75.1% accuracy, outperforming the strongest zero-shot baseline (GPT-4V with MMD-Agent at 74.0% F1) by 7.65 points while maintaining 34.3% false positive rate versus 97.3% for a judge-only baseline. Test set results (5,000 samples) confirm generalization with 81.44% F1 and 75.08% accuracy. Ablation studies show visual verification contributes 5.18 F1 points and retrieval-augmented reasoning contributes 2.97 points. Our results demonstrate that decomposed agentic reasoning with web retrieval can match supervised detector performance without domain-specific training, enabling misinformation detection across modalities where labeled data remains scarce.
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                标注高效的通用诚实对齐
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            Annotation-Efficient Universal Honesty Alignment
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shiyu Ni, Keping Bi, Jiafeng Guo, Minghao Tang, Jingtong Wu, Zengxin Han, Xueqi ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注LLM的诚实对齐，这属于模型安全性和可靠性范畴，而非推荐系统、搜索或广告的核心技术进展。虽然诚实对齐可能间接影响这些系统中LLM的应用可信度，但论文本身不涉及排序、检索、多模态建模或Transformer架构效率等关键技术方向，与当前关注点关联度较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 13:05:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17509v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17509v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Honesty alignment-the ability of large language models (LLMs) to recognize their knowledge boundaries and express calibrated confidence-is essential for trustworthy deployment. Existing methods either rely on training-free confidence estimation (e.g., token probabilities, self-consistency) or training-based calibration with correctness annotations. While effective, achieving universal honesty alignment with training-based calibration requires costly, large-scale labeling. To support annotation-efficient training, we introduce Elicitation-Then-Calibration (EliCal), a two-stage framework that first elicits internal confidence using inexpensive self-consistency supervision, then calibrates this confidence with a small set of correctness annotations. To support a large-scale study, we release HonestyBench, a benchmark covering ten free-form QA datasets with 560k training and 70k evaluation instances annotated with correctness and self-consistency signals. Experiments show that EliCal achieves near-optimal alignment with only 1k correctness annotations (0.18% of full supervision) and better alignment performance on unseen MMLU tasks than the calibration-only baseline, offering a scalable solution toward universal honesty alignment in LLMs.
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            <a href="https://www.alphaxiv.org/abs/2510.17489v1" target="_blank" rel="noopener noreferrer">
                DETree：通过树状结构分层表示学习检测人机协作文本
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            DETree: DEtecting Human-AI Collaborative Texts via Tree-Structured Hierarchical Representation Learning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yongxin He, Shan Zhang, Yixuan Cao, Lei Ma, Ping Luo
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注人机协作文本的检测，这属于内容识别和验证领域，与推荐系统、搜索或广告的核心技术关联度较低。虽然涉及表示学习技术，但其应用场景（检测人机协作文本）在推荐、搜索或广告中的直接应用潜力有限，更多偏向内容安全和验证方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 12:41:44
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                <a href="https://arxiv.org/abs/2510.17489v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17489v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Detecting AI-involved text is essential for combating misinformation, plagiarism, and academic misconduct. However, AI text generation includes diverse collaborative processes (AI-written text edited by humans, human-written text edited by AI, and AI-generated text refined by other AI), where various or even new LLMs could be involved. Texts generated through these varied processes exhibit complex characteristics, presenting significant challenges for detection. Current methods model these processes rather crudely, primarily employing binary classification (purely human vs. AI-involved) or multi-classification (treating human-AI collaboration as a new class). We observe that representations of texts generated through different processes exhibit inherent clustering relationships. Therefore, we propose DETree, a novel approach that models the relationships among different processes as a Hierarchical Affinity Tree structure, and introduces a specialized loss function that aligns text representations with this tree. To facilitate this learning, we developed RealBench, a comprehensive benchmark dataset that automatically incorporates a wide spectrum of hybrid texts produced through various human-AI collaboration processes. Our method improves performance in hybrid text detection tasks and significantly enhances robustness and generalization in out-of-distribution scenarios, particularly in few-shot learning conditions, further demonstrating the promise of training-based approaches in OOD settings. Our code and dataset are available at https://github.com/heyongxin233/DETree.
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            <a href="https://www.alphaxiv.org/abs/2510.17389v1" target="_blank" rel="noopener noreferrer">
                EduAdapt：一个用于评估大语言模型年级适应性水平的问题回答基准数据集
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            EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Numaan Naeem, Abdellah El Mekki, Muhammad Abdul-Mageed
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注教育领域的基准数据集构建和LLM评估，属于纯粹的评估基准研究。虽然涉及LLM技术，但缺乏明确的推荐系统、搜索或广告应用场景，且专注于教育领域的年级适应性评估这一特定任务，与当前关注的核心领域进展和直接应用方向关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:30:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17389v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17389v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">I.2.7</span></div>
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                    Large language models (LLMs) are transforming education by answering questions, explaining complex concepts, and generating content across a wide range of subjects. Despite strong performance on academic benchmarks, they often fail to tailor responses to students' grade levels. This is a critical need in K-12 education, where age-appropriate vocabulary and explanation are essential for effective learning. Existing models frequently produce outputs that are too advanced or vague for younger learners, and there are no standardized benchmarks to evaluate their ability to adjust across cognitive and developmental stages. To address this gap, we introduce EduAdapt, a benchmark of nearly 48k grade-labeled QA pairs across nine science subjects, spanning Grades 1-12 and grouped into four grade levels. We evaluate a diverse set of open-source LLMs on EduAdapt and find that while larger models generally perform better, they still struggle with generating suitable responses for early-grade students (Grades 1-5). Our work presents the first dataset and evaluation framework for assessing grade-level adaptability in LLMs, aiming to foster more developmentally aligned educational AI systems through better training and prompting strategies. EduAdapt code and datasets are publicly available at https://github.com/NaumanNaeem/EduAdapt.
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            <a href="https://www.alphaxiv.org/abs/2510.17289v1" target="_blank" rel="noopener noreferrer">
                通过多模态表示学习解决多方对话中的反社会行为
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            Addressing Antisocial Behavior in Multi-Party Dialogs Through Multimodal Representation Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hajar Bakarou, Mohamed Sinane El Messoussi, Anaïs Ollagnier
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注对话系统中的反社会行为检测，这属于内容安全/伦理范畴，属于明确的无关主题。虽然涉及多模态表示学习技术，但其核心应用场景（反社会行为检测）与推荐系统、搜索或广告的排名优化没有直接关联，且安全/伦理主题已被明确排除。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 08:27:38
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                <a href="https://arxiv.org/abs/2510.17289v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17289v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Antisocial behavior (ASB) on social media -- including hate speech, harassment, and cyberbullying -- poses growing risks to platform safety and societal well-being. Prior research has focused largely on networks such as X and Reddit, while \textit{multi-party conversational settings} remain underexplored due to limited data. To address this gap, we use \textit{CyberAgressionAdo-Large}, a French open-access dataset simulating ASB in multi-party conversations, and evaluate three tasks: \textit{abuse detection}, \textit{bullying behavior analysis}, and \textit{bullying peer-group identification}. We benchmark six text-based and eight graph-based \textit{representation-learning methods}, analyzing lexical cues, interactional dynamics, and their multimodal fusion. Results show that multimodal models outperform unimodal baselines. The late fusion model \texttt{mBERT + WD-SGCN} achieves the best overall results, with top performance on abuse detection (0.718) and competitive scores on peer-group identification (0.286) and bullying analysis (0.606). Error analysis highlights its effectiveness in handling nuanced ASB phenomena such as implicit aggression, role transitions, and context-dependent hostility.
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            <a href="https://www.alphaxiv.org/abs/2510.17263v1" target="_blank" rel="noopener noreferrer">
                TaxoAlign：基于语言模型的学术分类体系生成
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            TaxoAlign: Scholarly Taxonomy Generation Using Language Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Avishek Lahiri, Yufang Hou, Debarshi Kumar Sanyal
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于学术分类体系生成，属于特定领域的知识组织应用，与推荐系统、搜索或广告的核心进展关联度较低。虽然使用了语言模型技术，但其应用场景（学术分类）与商业推荐/搜索系统的实际需求差距较大，缺乏明确的跨领域应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 07:49:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17263v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17263v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Taxonomies play a crucial role in helping researchers structure and navigate knowledge in a hierarchical manner. They also form an important part in the creation of comprehensive literature surveys. The existing approaches to automatic survey generation do not compare the structure of the generated surveys with those written by human experts. To address this gap, we present our own method for automated taxonomy creation that can bridge the gap between human-generated and automatically-created taxonomies. For this purpose, we create the CS-TaxoBench benchmark which consists of 460 taxonomies that have been extracted from human-written survey papers. We also include an additional test set of 80 taxonomies curated from conference survey papers. We propose TaxoAlign, a three-phase topic-based instruction-guided method for scholarly taxonomy generation. Additionally, we propose a stringent automated evaluation framework that measures the structural alignment and semantic coherence of automatically generated taxonomies in comparison to those created by human experts. We evaluate our method and various baselines on CS-TaxoBench, using both automated evaluation metrics and human evaluation studies. The results show that TaxoAlign consistently surpasses the baselines on nearly all metrics. The code and data can be found at https://github.com/AvishekLahiri/TaxoAlign.
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            <a href="https://www.alphaxiv.org/abs/2510.17256v1" target="_blank" rel="noopener noreferrer">
                大语言模型的可解释性：生成可信解释的机遇与挑战
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            Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shahin Atakishiyev, Housam K. B. Babiker, Jiayi Dai, Nawshad Farruque, Teruaki H...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM可解释性和可信解释生成，这属于纯粹的LLM中心化主题，与推荐系统、搜索或广告的核心进展无关。虽然可解释性在理论上有潜在价值，但论文没有明确说明在RecSys/Search/Ads中的具体应用，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 07:43:53
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                <a href="https://arxiv.org/abs/2510.17256v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17256v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large language models have exhibited impressive performance across a broad range of downstream tasks in natural language processing. However, how a language model predicts the next token and generates content is not generally understandable by humans. Furthermore, these models often make errors in prediction and reasoning, known as hallucinations. These errors underscore the urgent need to better understand and interpret the intricate inner workings of language models and how they generate predictive outputs. Motivated by this gap, this paper investigates local explainability and mechanistic interpretability within Transformer-based large language models to foster trust in such models. In this regard, our paper aims to make three key contributions. First, we present a review of local explainability and mechanistic interpretability approaches and insights from relevant studies in the literature. Furthermore, we describe experimental studies on explainability and reasoning with large language models in two critical domains -- healthcare and autonomous driving -- and analyze the trust implications of such explanations for explanation receivers. Finally, we summarize current unaddressed issues in the evolving landscape of LLM explainability and outline the opportunities, critical challenges, and future directions toward generating human-aligned, trustworthy LLM explanations.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17252v1" target="_blank" rel="noopener noreferrer">
                新闻感受如何：理解多语言标题中的情感偏见以进行以人为本的媒体设计
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            How News Feels: Understanding Affective Bias in Multilingual Headlines for Human-Centered Media Design
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mohd Ruhul Ameen, Akif Islam, Abu Saleh Musa Miah, Ayesha Siddiqua, Jungpil Shin
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注新闻标题中的情感偏见分析和媒体设计，属于内容理解和情感分析领域。虽然情感分析在推荐系统中可能有一定应用，但论文聚焦于新闻媒体设计这一特定领域，与搜索、推荐或广告的核心技术进展关联度较低，且未涉及LLM、Transformer架构或异构数据统一建模等关键技术方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 07:40:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17252v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17252v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    News media often shape the public mood not only by what they report but by how they frame it. The same event can appear calm in one outlet and alarming in another, reflecting subtle emotional bias in reporting. Negative or emotionally charged headlines tend to attract more attention and spread faster, which in turn encourages outlets to frame stories in ways that provoke stronger reactions. This research explores that tendency through large-scale emotion analysis of Bengali news. Using zero-shot inference with Gemma-3 4B, we analyzed 300000 Bengali news headlines and their content to identify the dominant emotion and overall tone of each. The findings reveal a clear dominance of negative emotions, particularly anger, fear, and disappointment, and significant variation in how similar stories are emotionally portrayed across outlets. Based on these insights, we propose design ideas for a human-centered news aggregator that visualizes emotional cues and helps readers recognize hidden affective framing in daily news.
                </div>
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    </div>
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            <a href="https://www.alphaxiv.org/abs/2510.17210v1" target="_blank" rel="noopener noreferrer">
                智慧在于知晓不该说什么：通过注意力转移实现无幻觉大语言模型遗忘
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Wisdom is Knowing What not to Say: Hallucination-Free LLMs Unlearning via Attention Shifting
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenchen Tan, Youyang Qu, Xinghao Li, Hui Zhang, Shujie Cui, Cunjian Chen, Longx...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM幻觉问题，这属于纯粹的NLP中心话题，与推荐系统、搜索或广告的核心技术进展无关。虽然注意力转移机制可能有技术价值，但论文焦点是解决语言模型生成准确性问题，而非在推荐/搜索/广告领域的实际应用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 06:50:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17210v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17210v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The increase in computing power and the necessity of AI-assisted decision-making boost the growing application of large language models (LLMs). Along with this, the potential retention of sensitive data of LLMs has spurred increasing research into machine unlearning. However, existing unlearning approaches face a critical dilemma: Aggressive unlearning compromises model utility, while conservative strategies preserve utility but risk hallucinated responses. This significantly limits LLMs' reliability in knowledge-intensive applications. To address this, we introduce a novel Attention-Shifting (AS) framework for selective unlearning. AS is driven by two design objectives: (1) context-preserving suppression that attenuates attention to fact-bearing tokens without disrupting LLMs' linguistic structure; and (2) hallucination-resistant response shaping that discourages fabricated completions when queried about unlearning content. AS realizes these objectives through two attention-level interventions, which are importance-aware suppression applied to the unlearning set to reduce reliance on memorized knowledge and attention-guided retention enhancement that reinforces attention toward semantically essential tokens in the retained dataset to mitigate unintended degradation. These two components are jointly optimized via a dual-loss objective, which forms a soft boundary that localizes unlearning while preserving unrelated knowledge under representation superposition. Experimental results show that AS improves performance preservation over the state-of-the-art unlearning methods, achieving up to 15% higher accuracy on the ToFU benchmark and 10% on the TDEC benchmark, while maintaining competitive hallucination-free unlearning effectiveness. Compared to existing methods, AS demonstrates a superior balance between unlearning effectiveness, generalization, and response reliability.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17062v1" target="_blank" rel="noopener noreferrer">
                基于推理的语言模型思维行为研究用于社会偏见缓解
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Investigating Thinking Behaviours of Reasoning-Based Language Models for Social Bias Mitigation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guoqing Luo, Iffat Maab, Lili Mou, Junichi Yamagishi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注社会偏见缓解，这属于公平性和伦理范畴，被明确列为不相关主题。虽然涉及语言模型，但核心应用是偏见缓解而非推荐系统、搜索或广告中的实际应用。没有证据表明该技术对推荐、搜索或广告系统有直接或间接的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 00:33:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17062v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17062v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    While reasoning-based large language models excel at complex tasks through an internal, structured thinking process, a concerning phenomenon has emerged that such a thinking process can aggregate social stereotypes, leading to biased outcomes. However, the underlying behaviours of these language models in social bias scenarios remain underexplored. In this work, we systematically investigate mechanisms within the thinking process behind this phenomenon and uncover two failure patterns that drive social bias aggregation: 1) stereotype repetition, where the model relies on social stereotypes as its primary justification, and 2) irrelevant information injection, where it fabricates or introduces new details to support a biased narrative. Building on these insights, we introduce a lightweight prompt-based mitigation approach that queries the model to review its own initial reasoning against these specific failure patterns. Experiments on question answering (BBQ and StereoSet) and open-ended (BOLD) benchmarks show that our approach effectively reduces bias while maintaining or improving accuracy.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17771v1" target="_blank" rel="noopener noreferrer">
                眼见不为实：探究视觉语言模型中视觉注意力与答案正确性之间的脱节
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Seeing but Not Believing: Probing the Disconnect Between Visual Attention and Answer Correctness in VLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhining Liu, Ziyi Chen, Hui Liu, Chen Luo, Xianfeng Tang, Suhang Wang, Joy Zeng,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究视觉语言模型中的注意力机制与答案正确性的关系，属于VLM评估和诊断范畴。虽然涉及视觉模态，但论文聚焦于模型诊断而非技术应用，缺乏明确的推荐/搜索/广告应用潜力。该研究更偏向于模型理解而非实际系统改进，与当前关注的直接应用或使能技术关联度较低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:31:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17771v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17771v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision-Language Models (VLMs) achieve strong results on multimodal tasks such as visual question answering, yet they can still fail even when the correct visual evidence is present. In this work, we systematically investigate whether these failures arise from not perceiving the evidence or from not leveraging it effectively. By examining layer-wise attention dynamics, we find that shallow layers focus primarily on text, while deeper layers sparsely but reliably attend to localized evidence regions. Surprisingly, VLMs often perceive the visual evidence when outputting incorrect answers, a phenomenon we term ``seeing but not believing'' that widely exists in major VLM families. Building on this, we introduce an inference-time intervention that highlights deep-layer evidence regions through selective attention-based masking. It requires no training and consistently improves accuracy across multiple families, including LLaVA, Qwen, Gemma, and InternVL. These results show that VLMs encode reliable evidence internally but under-utilize it, making such signals explicit can bridge the gap between perception and reasoning, advancing the diagnostic understanding and reliability of VLMs.
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            <a href="https://www.alphaxiv.org/abs/2510.17739v1" target="_blank" rel="noopener noreferrer">
                基于矩阵分解的联合多条件表示建模用于视觉地点识别
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Joint Multi-Condition Representation Modelling via Matrix Factorisation for Visual Place Recognition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Timur Ismagilov, Shakaiba Majeed, Michael Milford, Tan Viet Tuyen Nguyen, Sarvap...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉地点识别，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告没有直接关联。虽然矩阵分解技术在某些推荐系统中有所应用，但该论文的视觉焦点和应用场景使其与当前关注的核心领域相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:50:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17739v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17739v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We address multi-reference visual place recognition (VPR), where reference sets captured under varying conditions are used to improve localisation performance. While deep learning with large-scale training improves robustness, increasing data diversity and model complexity incur extensive computational cost during training and deployment. Descriptor-level fusion via voting or aggregation avoids training, but often targets multi-sensor setups or relies on heuristics with limited gains under appearance and viewpoint change. We propose a training-free, descriptor-agnostic approach that jointly models places using multiple reference descriptors via matrix decomposition into basis representations, enabling projection-based residual matching. We also introduce SotonMV, a structured benchmark for multi-viewpoint VPR. On multi-appearance data, our method improves Recall@1 by up to ~18% over single-reference and outperforms multi-reference baselines across appearance and viewpoint changes, with gains of ~5% on unstructured data, demonstrating strong generalisation while remaining lightweight.
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            <a href="https://www.alphaxiv.org/abs/2510.17722v1" target="_blank" rel="noopener noreferrer">
                MT-Video-Bench：用于评估多轮对话中多模态大语言模型整体视频理解能力的基准
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        <div class="mb-2 text-base text-gray-700">
            MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yaning Pan, Zekun Wang, Qianqian Xie, Yongqian Wen, Yuanxing Zhang, Guohui Zhang...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态LLM的视频理解基准测试，属于纯粹的评估基准范畴。虽然涉及多模态和对话能力，但缺乏明确的推荐系统、搜索或广告应用场景，且基准测试本身属于评估方法而非核心技术进展。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:38:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17722v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17722v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The recent development of Multimodal Large Language Models (MLLMs) has significantly advanced AI's ability to understand visual modalities. However, existing evaluation benchmarks remain limited to single-turn question answering, overlooking the complexity of multi-turn dialogues in real-world scenarios. To bridge this gap, we introduce MT-Video-Bench, a holistic video understanding benchmark for evaluating MLLMs in multi-turn dialogues. Specifically, our MT-Video-Bench mainly assesses six core competencies that focus on perceptivity and interactivity, encompassing 987 meticulously curated multi-turn dialogues from diverse domains. These capabilities are rigorously aligned with real-world applications, such as interactive sports analysis and multi-turn video-based intelligent tutoring. With MT-Video-Bench, we extensively evaluate various state-of-the-art open-source and closed-source MLLMs, revealing their significant performance discrepancies and limitations in handling multi-turn video dialogues. The benchmark will be publicly available to foster future research.
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            <a href="https://www.alphaxiv.org/abs/2510.17699v1" target="_blank" rel="noopener noreferrer">
                GAS：通过广义对抗求解器改进扩散常微分方程的离散化
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            <i class="fa fa-star mr-1"></i>2/10
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            GAS: Improving Discretization of Diffusion ODEs via Generalized Adversarial Solver
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aleksandr Oganov, Ilya Bykov, Eva Neudachina, Mishan Aliev, Alexander Tolmachev,...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于扩散模型的数值求解方法改进，属于生成模型的技术优化。虽然扩散模型在内容生成中有应用，但论文标题显示其核心是ODE离散化的数学优化，与推荐系统、搜索或广告的排序、匹配、用户建模等核心问题缺乏直接关联。这种数值方法改进对RecSys/Search/Ads的实际应用潜力非常有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:14:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17699v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17699v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    While diffusion models achieve state-of-the-art generation quality, they still suffer from computationally expensive sampling. Recent works address this issue with gradient-based optimization methods that distill a few-step ODE diffusion solver from the full sampling process, reducing the number of function evaluations from dozens to just a few. However, these approaches often rely on intricate training techniques and do not explicitly focus on preserving fine-grained details. In this paper, we introduce the Generalized Solver: a simple parameterization of the ODE sampler that does not require additional training tricks and improves quality over existing approaches. We further combine the original distillation loss with adversarial training, which mitigates artifacts and enhances detail fidelity. We call the resulting method the Generalized Adversarial Solver and demonstrate its superior performance compared to existing solver training methods under similar resource constraints. Code is available at https://github.com/3145tttt/GAS.
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            <a href="https://www.alphaxiv.org/abs/2510.17611v1" target="_blank" rel="noopener noreferrer">
                一法通，异常全检测：面向全频谱无监督异常检测的统一框架
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            One Dinomaly2 Detect Them All: A Unified Framework for Full-Spectrum Unsupervised Anomaly Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jia Guo, Shuai Lu, Lei Fan, Zelin Li, Donglin Di, Yang Song, Weihang Zhang, Wenb...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于通用的无监督异常检测框架，属于机器学习的基础研究领域。虽然异常检测在技术上有一定通用性，但论文标题未表明与推荐系统、搜索或广告的具体关联，也未提及LLM、Transformer架构或异构数据建模等核心技术。缺乏明确的RecSys/Search/Ads应用场景或技术连接点。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:57:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17611v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17611v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Unsupervised anomaly detection (UAD) has evolved from building specialized single-class models to unified multi-class models, yet existing multi-class models significantly underperform the most advanced one-for-one counterparts. Moreover, the field has fragmented into specialized methods tailored to specific scenarios (multi-class, 3D, few-shot, etc.), creating deployment barriers and highlighting the need for a unified solution. In this paper, we present Dinomaly2, the first unified framework for full-spectrum image UAD, which bridges the performance gap in multi-class models while seamlessly extending across diverse data modalities and task settings. Guided by the "less is more" philosophy, we demonstrate that the orchestration of five simple element achieves superior performance in a standard reconstruction-based framework. This methodological minimalism enables natural extension across diverse tasks without modification, establishing that simplicity is the foundation of true universality. Extensive experiments on 12 UAD benchmarks demonstrate Dinomaly2's full-spectrum superiority across multiple modalities (2D, multi-view, RGB-3D, RGB-IR), task settings (single-class, multi-class, inference-unified multi-class, few-shot) and application domains (industrial, biological, outdoor). For example, our multi-class model achieves unprecedented 99.9% and 99.3% image-level (I-) AUROC on MVTec-AD and VisA respectively. For multi-view and multi-modal inspection, Dinomaly2 demonstrates state-of-the-art performance with minimum adaptations. Moreover, using only 8 normal examples per class, our method surpasses previous full-shot models, achieving 98.7% and 97.4% I-AUROC on MVTec-AD and VisA. The combination of minimalistic design, computational scalability, and universal applicability positions Dinomaly2 as a unified solution for the full spectrum of real-world anomaly detection applications.
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            <a href="https://www.alphaxiv.org/abs/2510.17603v1" target="_blank" rel="noopener noreferrer">
                ShapeCraft：用于结构化、纹理化和交互式3D建模的LLM智能体
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            ShapeCraft: LLM Agents for Structured, Textured and Interactive 3D Modeling
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuyuan Zhang, Chenhan Jiang, Zuoou Li, Jiankang Deng
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注3D建模和LLM智能体在图形领域的应用，属于纯粹的3D视觉和图形生成范畴。虽然涉及LLM技术，但缺乏与推荐系统、搜索或广告领域的直接关联，且3D建模应用超出了指定的相关技术领域范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:51:14
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                <a href="https://arxiv.org/abs/2510.17603v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17603v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D generation from natural language offers significant potential to reduce expert manual modeling efforts and enhance accessibility to 3D assets. However, existing methods often yield unstructured meshes and exhibit poor interactivity, making them impractical for artistic workflows. To address these limitations, we represent 3D assets as shape programs and introduce ShapeCraft, a novel multi-agent framework for text-to-3D generation. At its core, we propose a Graph-based Procedural Shape (GPS) representation that decomposes complex natural language into a structured graph of sub-tasks, thereby facilitating accurate LLM comprehension and interpretation of spatial relationships and semantic shape details. Specifically, LLM agents hierarchically parse user input to initialize GPS, then iteratively refine procedural modeling and painting to produce structured, textured, and interactive 3D assets. Qualitative and quantitative experiments demonstrate ShapeCraft's superior performance in generating geometrically accurate and semantically rich 3D assets compared to existing LLM-based agents. We further show the versatility of ShapeCraft through examples of animated and user-customized editing, highlighting its potential for broader interactive applications.
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            <a href="https://www.alphaxiv.org/abs/2510.17519v1" target="_blank" rel="noopener noreferrer">
                MUG-V 10B：大型视频生成模型的高效训练流水线
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            MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yongshun Zhang, Zhongyi Fan, Yonghang Zhang, Zhangzikang Li, Weifeng Chen, Zhong...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频生成模型的训练效率优化，属于纯粹的视觉内容生成领域。虽然训练效率技术本身有价值，但视频生成与推荐系统、搜索或广告的核心排序任务关联度极低，且论文标题未表明任何潜在的跨模态应用或与异构数据建模的关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 13:20:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17519v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17519v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    In recent years, large-scale generative models for visual content (\textit{e.g.,} images, videos, and 3D objects/scenes) have made remarkable progress. However, training large-scale video generation models remains particularly challenging and resource-intensive due to cross-modal text-video alignment, the long sequences involved, and the complex spatiotemporal dependencies. To address these challenges, we present a training framework that optimizes four pillars: (i) data processing, (ii) model architecture, (iii) training strategy, and (iv) infrastructure for large-scale video generation models. These optimizations delivered significant efficiency gains and performance improvements across all stages of data preprocessing, video compression, parameter scaling, curriculum-based pretraining, and alignment-focused post-training. Our resulting model, MUG-V 10B, matches recent state-of-the-art video generators overall and, on e-commerce-oriented video generation tasks, surpasses leading open-source baselines in human evaluations. More importantly, we open-source the complete stack, including model weights, Megatron-Core-based large-scale training code, and inference pipelines for video generation and enhancement. To our knowledge, this is the first public release of large-scale video generation training code that exploits Megatron-Core to achieve high training efficiency and near-linear multi-node scaling, details are available in \href{https://github.com/Shopee-MUG/MUG-V}{our webpage}.
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            <a href="https://www.alphaxiv.org/abs/2510.17501v1" target="_blank" rel="noopener noreferrer">
                面向零样本视频摘要的上下文感知伪标签评分
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            Context-Aware Pseudo-Label Scoring for Zero-Shot Video Summarization
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuanli Wu, Long Zhang, Yue Du, Bin Li
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视频摘要任务，属于纯粹的视觉应用领域，与推荐系统、搜索或广告的核心技术无直接关联。虽然标题中提到'上下文感知'和'零样本'等技术概念，但这些在论文中被应用于视频内容处理，而非用户行为建模或排名优化等RecSys/Search/Ads核心问题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 12:54:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17501v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17501v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    With the rapid proliferation of video content across social media, surveillance, and education platforms, efficiently summarizing long videos into concise yet semantically faithful surrogates has become increasingly vital. Existing supervised methods achieve strong in-domain accuracy by learning from dense annotations but suffer from high labeling costs and limited cross-dataset generalization, while unsupervised approaches, though label-free, often fail to capture high-level human semantics and fine-grained narrative cues. More recently, zero-shot prompting pipelines have leveraged large language models (LLMs) for training-free video summarization, yet remain highly sensitive to handcrafted prompt templates and dataset-specific score normalization. To overcome these limitations, we introduce a rubric-guided, pseudo-labeled prompting framework that transforms a small subset of ground-truth annotations into high-confidence pseudo labels, which are aggregated into structured, dataset-adaptive scoring rubrics guiding interpretable scene evaluation. During inference, first and last segments are scored based solely on their descriptions, whereas intermediate ones incorporate brief contextual summaries of adjacent scenes to assess narrative progression and redundancy. This contextual prompting enables the LLM to balance local salience and global coherence without parameter tuning. On SumMe and TVSum, our method achieves F1 scores of \textbf{57.58} and \textbf{63.05}, surpassing unsupervised and prior zero-shot baselines while approaching supervised performance. The results demonstrate that rubric-guided pseudo labeling effectively stabilizes LLM-based scoring and establishes a general, interpretable zero-shot paradigm for video summarization.
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            <a href="https://www.alphaxiv.org/abs/2510.17484v1" target="_blank" rel="noopener noreferrer">
                分割-融合-传输：通过双重聚类和最优传输对齐实现无标注显著性检测
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            Split-Fuse-Transport: Annotation-Free Saliency via Dual Clustering and Optimal Transport Alignment
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Muhammad Umer Ramzan, Ali Zia, Abdelwahed Khamis, Noman Ali, Usman Ali, Wei Xian...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的显著性检测任务，属于纯粹的视觉技术范畴。虽然最优传输方法在理论上有潜在的跨模态对齐应用，但论文标题明确指向视觉显著性检测，与推荐系统、搜索或广告的核心技术领域没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 12:27:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17484v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17484v1
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Salient object detection (SOD) aims to segment visually prominent regions in images and serves as a foundational task for various computer vision applications. We posit that SOD can now reach near-supervised accuracy without a single pixel-level label, but only when reliable pseudo-masks are available. We revisit the prototype-based line of work and make two key observations. First, boundary pixels and interior pixels obey markedly different geometry; second, the global consistency enforced by optimal transport (OT) is underutilized if prototype quality is weak. To address this, we introduce POTNet, an adaptation of Prototypical Optimal Transport that replaces POT's single k-means step with an entropy-guided dual-clustering head: high-entropy pixels are organized by spectral clustering, low-entropy pixels by k-means, and the two prototype sets are subsequently aligned by OT. This split-fuse-transport design yields sharper, part-aware pseudo-masks in a single forward pass, without handcrafted priors. Those masks supervise a standard MaskFormer-style encoder-decoder, giving rise to AutoSOD, an end-to-end unsupervised SOD pipeline that eliminates SelfMask's offline voting yet improves both accuracy and training efficiency. Extensive experiments on five benchmarks show that AutoSOD outperforms unsupervised methods by up to 26% and weakly supervised methods by up to 36% in F-measure, further narrowing the gap to fully supervised models.
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            <a href="https://www.alphaxiv.org/abs/2510.17482v1" target="_blank" rel="noopener noreferrer">
                SparseWorld：一种由稀疏动态查询驱动的灵活、自适应且高效的4D占用世界模型
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenxu Dang, Haiyan Liu, Guangjun Bao, Pei An, Xinyue Tang, Jie Ma, Bingchuan Su...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注4D占用世界模型和稀疏动态查询技术，属于计算机视觉和3D场景理解领域。虽然提到了高效建模技术，但缺乏与推荐系统、搜索或广告领域的直接关联，也没有明确说明这些技术如何应用于异构数据处理或Transformer架构改进。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 12:26:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17482v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17482v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Semantic occupancy has emerged as a powerful representation in world models for its ability to capture rich spatial semantics. However, most existing occupancy world models rely on static and fixed embeddings or grids, which inherently limit the flexibility of perception. Moreover, their ``in-place classification" over grids exhibits a potential misalignment with the dynamic and continuous nature of real scenarios.In this paper, we propose SparseWorld, a novel 4D occupancy world model that is flexible, adaptive, and efficient, powered by sparse and dynamic queries. We propose a Range-Adaptive Perception module, in which learnable queries are modulated by the ego vehicle states and enriched with temporal-spatial associations to enable extended-range perception. To effectively capture the dynamics of the scene, we design a State-Conditioned Forecasting module, which replaces classification-based forecasting with regression-guided formulation, precisely aligning the dynamic queries with the continuity of the 4D environment. In addition, We specifically devise a Temporal-Aware Self-Scheduling training strategy to enable smooth and efficient training. Extensive experiments demonstrate that SparseWorld achieves state-of-the-art performance across perception, forecasting, and planning tasks. Comprehensive visualizations and ablation studies further validate the advantages of SparseWorld in terms of flexibility, adaptability, and efficiency. The code is available at https://github.com/MSunDYY/SparseWorld.
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            <a href="https://www.alphaxiv.org/abs/2510.17439v1" target="_blank" rel="noopener noreferrer">
                从空间到行动：基于空间基础先验的视觉-语言-行动模型接地
            </a>
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        <div class="mb-2 text-base text-gray-700">
            From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhengshen Zhang, Hao Li, Yalun Dai, Zhengbang Zhu, Lei Zhou, Chenchen Liu, Dong ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉-语言-行动模型和空间基础先验，这属于机器人技术和具身AI领域。虽然标题提到多模态建模，但其核心是视觉动作空间接地，与推荐系统、搜索或广告没有直接关联。该技术可能对处理视觉内容的推荐系统有间接启发，但应用潜力非常有限且不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 11:26:45
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17439v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17439v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptability. Recent 3D integration techniques for VLAs either require specialized sensors and transfer poorly across modalities, or inject weak cues that lack geometry and degrade vision-language alignment. In this work, we introduce FALCON (From Spatial to Action), a novel paradigm that injects rich 3D spatial tokens into the action head. FALCON leverages spatial foundation models to deliver strong geometric priors from RGB alone, and includes an Embodied Spatial Model that can optionally fuse depth, or pose for higher fidelity when available, without retraining or architectural changes. To preserve language reasoning, spatial tokens are consumed by a Spatial-Enhanced Action Head rather than being concatenated into the vision-language backbone. These designs enable FALCON to address limitations in spatial representation, modality transferability, and alignment. In comprehensive evaluations across three simulation benchmarks and eleven real-world tasks, our proposed FALCON achieves state-of-the-art performance, consistently surpasses competitive baselines, and remains robust under clutter, spatial-prompt conditioning, and variations in object scale and height.
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            <a href="https://www.alphaxiv.org/abs/2510.17422v1" target="_blank" rel="noopener noreferrer">
                DeepDetect：学习一体化密集关键点
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            DeepDetect: Learning All-in-One Dense Keypoints
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shaharyar Ahmed Khan Tareen, Filza Khan Tareen
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的密集关键点检测，属于纯粹的视觉技术领域。虽然关键点检测在一般计算机视觉中有应用，但论文标题没有表明与推荐系统、搜索或广告的明确关联，也没有涉及Transformer架构、LLM技术或异构数据建模等当前关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 11:09:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17422v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17422v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Keypoint detection is the foundation of many computer vision tasks, including image registration, structure-from motion, 3D reconstruction, visual odometry, and SLAM. Traditional detectors (SIFT, SURF, ORB, BRISK, etc.) and learning based methods (SuperPoint, R2D2, LF-Net, D2-Net, etc.) have shown strong performance yet suffer from key limitations: sensitivity to photometric changes, low keypoint density and repeatability, limited adaptability to challenging scenes, and lack of semantic understanding, often failing to prioritize visually important regions. We present DeepDetect, an intelligent, all-in-one, dense keypoint detector that unifies the strengths of classical detectors using deep learning. Firstly, we create ground-truth masks by fusing outputs of 7 keypoint and 2 edge detectors, extracting diverse visual cues from corners and blobs to prominent edges and textures in the images. Afterwards, a lightweight and efficient model: ESPNet, is trained using these masks as labels, enabling DeepDetect to focus semantically on images while producing highly dense keypoints, that are adaptable to diverse and visually degraded conditions. Evaluations on the Oxford Affine Covariant Regions dataset demonstrate that DeepDetect surpasses other detectors in keypoint density, repeatability, and the number of correct matches, achieving maximum values of 0.5143 (average keypoint density), 0.9582 (average repeatability), and 59,003 (correct matches).
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            <a href="https://www.alphaxiv.org/abs/2510.17384v1" target="_blank" rel="noopener noreferrer">
                弱监督功能可供性定位的闭环迁移
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        <div class="mb-2 text-base text-gray-700">
            Closed-Loop Transfer for Weakly-supervised Affordance Grounding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiajin Tang, Zhengxuan Wei, Ge Zheng, Sibei Yang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的功能可供性定位问题，属于视觉理解任务。虽然标题提到弱监督和迁移学习，但这些技术在当前标题语境下主要应用于视觉场景理解，与推荐系统、搜索或广告的核心技术栈没有直接关联。该工作缺乏明确的跨模态或多模态建模视角，难以直接应用于异构数据处理或推荐系统场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:21:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17384v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17384v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Humans can perform previously unexperienced interactions with novel objects simply by observing others engage with them. Weakly-supervised affordance grounding mimics this process by learning to locate object regions that enable actions on egocentric images, using exocentric interaction images with image-level annotations. However, extracting affordance knowledge solely from exocentric images and transferring it one-way to egocentric images limits the applicability of previous works in complex interaction scenarios. Instead, this study introduces LoopTrans, a novel closed-loop framework that not only transfers knowledge from exocentric to egocentric but also transfers back to enhance exocentric knowledge extraction. Within LoopTrans, several innovative mechanisms are introduced, including unified cross-modal localization and denoising knowledge distillation, to bridge domain gaps between object-centered egocentric and interaction-centered exocentric images while enhancing knowledge transfer. Experiments show that LoopTrans achieves consistent improvements across all metrics on image and video benchmarks, even handling challenging scenarios where object interaction regions are fully occluded by the human body.
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            <a href="https://www.alphaxiv.org/abs/2510.17383v1" target="_blank" rel="noopener noreferrer">
                超越合成的潜在空间：从生成对抗网络到扩散模型
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        <div class="mb-2 text-base text-gray-700">
            Latent Spaces Beyond Synthesis: From GANs to Diffusion Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ludovica Schaerf
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注生成模型（GANs和扩散模型）的潜在空间分析，这属于AIGC和内容生成领域。虽然潜在空间表示在某些推荐系统中可能用于特征学习，但论文标题明确聚焦于合成应用而非推荐/搜索/广告的直接应用。没有明确证据表明该工作会应用于推荐系统的用户表示学习或物品嵌入。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:20:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17383v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17383v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.CY</span></div>
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                    This paper examines the evolving nature of internal representations in generative visual models, focusing on the conceptual and technical shift from GANs and VAEs to diffusion-based architectures. Drawing on Beatrice Fazi's account of synthesis as the amalgamation of distributed representations, we propose a distinction between "synthesis in a strict sense", where a compact latent space wholly determines the generative process, and "synthesis in a broad sense," which characterizes models whose representational labor is distributed across layers. Through close readings of model architectures and a targeted experimental setup that intervenes in layerwise representations, we show how diffusion models fragment the burden of representation and thereby challenge assumptions of unified internal space. By situating these findings within media theoretical frameworks and critically engaging with metaphors such as the latent space and the Platonic Representation Hypothesis, we argue for a reorientation of how generative AI is understood: not as a direct synthesis of content, but as an emergent configuration of specialized processes.
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                超越真实人脸：合成数据集可在不牺牲隐私的前提下实现可靠的识别性能
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            Beyond Real Faces: Synthetic Datasets Can Achieve Reliable Recognition Performance without Privacy Compromise
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Paweł Borsukiewicz, Fadi Boutros, Iyiola E. Olatunji, Charles Beumier, Wendkûuni...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注合成数据在面部识别中的应用，这属于计算机视觉领域，与推荐系统、搜索或广告的核心技术关联较弱。虽然合成数据技术可能间接应用于用户行为模拟或数据增强，但论文标题明确聚焦于面部识别这一特定任务，缺乏与RecSys/Search/Ads领域的直接联系或明确的潜在应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:08:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17372v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17372v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The deployment of facial recognition systems has created an ethical dilemma: achieving high accuracy requires massive datasets of real faces collected without consent, leading to dataset retractions and potential legal liabilities under regulations like GDPR. While synthetic facial data presents a promising privacy-preserving alternative, the field lacks comprehensive empirical evidence of its viability. This study addresses this critical gap through extensive evaluation of synthetic facial recognition datasets. We present a systematic literature review identifying 25 synthetic facial recognition datasets (2018-2025), combined with rigorous experimental validation. Our methodology examines seven key requirements for privacy-preserving synthetic data: identity leakage prevention, intra-class variability, identity separability, dataset scale, ethical data sourcing, bias mitigation, and benchmark reliability. Through experiments involving over 10 million synthetic samples, extended by a comparison of results reported on five standard benchmarks, we provide the first comprehensive empirical assessment of synthetic data's capability to replace real datasets. Best-performing synthetic datasets (VariFace, VIGFace) achieve recognition accuracies of 95.67% and 94.91% respectively, surpassing established real datasets including CASIA-WebFace (94.70%). While those images remain private, publicly available alternatives Vec2Face (93.52%) and CemiFace (93.22%) come close behind. Our findings reveal that they ensure proper intra-class variability while maintaining identity separability. Demographic bias analysis shows that, even though synthetic data inherits limited biases, it offers unprecedented control for bias mitigation through generation parameters. These results establish synthetic facial data as a scientifically viable and ethically imperative alternative for facial recognition research.
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            <a href="https://www.alphaxiv.org/abs/2510.17363v1" target="_blank" rel="noopener noreferrer">
                M2H：基于高效窗口跨任务注意力的单目空间感知多任务学习
            </a>
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            M2H: Multi-Task Learning with Efficient Window-Based Cross-Task Attention for Monocular Spatial Perception
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>U. V. B. L Udugama, George Vosselman, Francesco Nex
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的单目空间感知和多任务学习，虽然提到了高效的注意力机制，但其核心应用场景是空间感知而非推荐系统、搜索或广告。窗口注意力机制在理论上可能对序列建模有启发，但缺乏明确的RecSys/Search/Ads应用连接。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:03:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17363v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17363v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.RO</span></div>
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                    Deploying real-time spatial perception on edge devices requires efficient multi-task models that leverage complementary task information while minimizing computational overhead. This paper introduces Multi-Mono-Hydra (M2H), a novel multi-task learning framework designed for semantic segmentation and depth, edge, and surface normal estimation from a single monocular image. Unlike conventional approaches that rely on independent single-task models or shared encoder-decoder architectures, M2H introduces a Window-Based Cross-Task Attention Module that enables structured feature exchange while preserving task-specific details, improving prediction consistency across tasks. Built on a lightweight ViT-based DINOv2 backbone, M2H is optimized for real-time deployment and serves as the foundation for monocular spatial perception systems supporting 3D scene graph construction in dynamic environments. Comprehensive evaluations show that M2H outperforms state-of-the-art multi-task models on NYUDv2, surpasses single-task depth and semantic baselines on Hypersim, and achieves superior performance on the Cityscapes dataset, all while maintaining computational efficiency on laptop hardware. Beyond benchmarks, M2H is validated on real-world data, demonstrating its practicality in spatial perception tasks.
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            <a href="https://www.alphaxiv.org/abs/2510.17347v1" target="_blank" rel="noopener noreferrer">
                探索事件模态中缺失的语义
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            Exploring The Missing Semantics In Event Modality
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingqian Wu, Shengpeng Xu, Yunbo Jia, Edmund Y. Lam
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及事件模态和语义理解，可能属于事件表示学习或时序建模领域。虽然事件序列建模在推荐系统中有所应用，但标题过于宽泛，没有明确指向推荐、搜索或广告的具体技术，也没有表明涉及LLM或Transformer架构的进展，因此相关性较低。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 09:45:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17347v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17347v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Event cameras offer distinct advantages such as low latency, high dynamic range, and efficient motion capture. However, event-to-video reconstruction (E2V), a fundamental event-based vision task, remains challenging, particularly for reconstructing and recovering semantic information. This is primarily due to the nature of the event camera, as it only captures intensity changes, ignoring static objects and backgrounds, resulting in a lack of semantic information in captured event modality. Further, semantic information plays a crucial role in video and frame reconstruction, yet is often overlooked by existing E2V approaches. To bridge this gap, we propose Semantic-E2VID, an E2V framework that explores the missing visual semantic knowledge in event modality and leverages it to enhance event-to-video reconstruction. Specifically, Semantic-E2VID introduces a cross-modal feature alignment (CFA) module to transfer the robust visual semantics from a frame-based vision foundation model, the Segment Anything Model (SAM), to the event encoder, while aligning the high-level features from distinct modalities. To better utilize the learned semantic feature, we further propose a semantic-aware feature fusion (SFF) block to integrate learned semantics in frame modality to form event representations with rich semantics that can be decoded by the event decoder. Further, to facilitate the reconstruction of semantic information, we propose a novel Semantic Perceptual E2V Supervision that helps the model to reconstruct semantic details by leveraging SAM-generated categorical labels. Extensive experiments demonstrate that Semantic-E2VID significantly enhances frame quality, outperforming state-of-the-art E2V methods across multiple benchmarks. The sample code is included in the supplementary material.
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            <a href="https://www.alphaxiv.org/abs/2510.17332v1" target="_blank" rel="noopener noreferrer">
                iDETEX：赋能多模态大语言模型实现智能详细可解释的图像质量评估
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            iDETEX: Empowering MLLMs for Intelligent DETailed EXplainable IQA
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhaoran Zhao, Xinli Yue, Jianhui Sun, Yuhao Xie, Tao Shao, Liangchao Yao, Fan Xi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态大语言模型在图像质量评估领域的应用，属于计算机视觉和图像处理范畴。虽然提到了可解释性，但核心应用场景（图像质量评估）与推荐系统、搜索或广告没有直接关联，且不涉及处理异构数据或Transformer架构的改进。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 09:26:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17332v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17332v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Image Quality Assessment (IQA) has progressed from scalar quality prediction to more interpretable, human-aligned evaluation paradigms. In this work, we address the emerging challenge of detailed and explainable IQA by proposing iDETEX-a unified multimodal large language model (MLLM) capable of simultaneously performing three key tasks: quality grounding, perception, and description. To facilitate efficient and generalizable training across these heterogeneous subtasks, we design a suite of task-specific offline augmentation modules and a data mixing strategy. These are further complemented by online enhancement strategies to fully exploit multi-sourced supervision. We validate our approach on the large-scale ViDA-UGC benchmark, where iDETEX achieves state-of-the-art performance across all subtasks. Our model ranks first in the ICCV MIPI 2025 Detailed Image Quality Assessment Challenge, demonstrating its effectiveness and robustness in delivering accurate and interpretable quality assessments.
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            <a href="https://www.alphaxiv.org/abs/2510.17330v1" target="_blank" rel="noopener noreferrer">
                CharDiff：一种用于车牌图像复原的字符级引导扩散模型
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            CharDiff: A Diffusion Model with Character-Level Guidance for License Plate Image Restoration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gyuhwan Park, Kihyun Na, Injung Kim
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的图像复原任务，特别是车牌图像处理，这属于纯粹的视觉应用范畴。虽然扩散模型是生成模型的重要进展，但该工作没有展示与推荐系统、搜索或广告的潜在应用连接，其字符级引导机制也主要针对车牌识别这一特定视觉任务。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 09:23:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17330v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17330v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    The significance of license plate image restoration goes beyond the preprocessing stage of License Plate Recognition (LPR) systems, as it also serves various purposes, including increasing evidential value, enhancing the clarity of visual interface, and facilitating further utilization of license plate images. We propose a novel diffusion-based framework with character-level guidance, CharDiff, which effectively restores and recognizes severely degraded license plate images captured under realistic conditions. CharDiff leverages fine-grained character-level priors extracted through external segmentation and Optical Character Recognition (OCR) modules tailored for low-quality license plate images. For precise and focused guidance, CharDiff incorporates a novel Character-guided Attention through Region-wise Masking (CHARM) module, which ensures that each character's guidance is restricted to its own region, thereby avoiding interference with other regions. In experiments, CharDiff significantly outperformed the baseline restoration models in both restoration quality and recognition accuracy, achieving a 28% relative reduction in CER on the Roboflow-LP dataset, compared to the best-performing baseline model. These results indicate that the structured character-guided conditioning effectively enhances the robustness of diffusion-based license plate restoration and recognition in practical deployment scenarios.
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                LongInsightBench：一个用于评估全模态模型在以人为本的长视频理解上的综合基准
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            LongInsightBench: A Comprehensive Benchmark for Evaluating Omni-Modal Models on Human-Centric Long-Video Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>ZhaoYang Han, Qihan Lin, Hao Liang, Bowen Chen, Zhou Liu, Wentao Zhang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注长视频理解的基准测试，属于纯粹的评估基准范畴，这在无关主题中明确排除。虽然提到了'全模态模型'可能涉及多模态处理，但核心焦点是视频理解基准而非推荐系统、搜索或广告的直接应用，也没有明确展示在推荐/搜索领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 08:49:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17305v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17305v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.MM</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce \textbf{LongInsightBench}, the first benchmark designed to assess models' ability to understand long videos, with a focus on human language, viewpoints, actions, and other contextual elements, while integrating \textbf{visual, audio, and text} modalities. Our benchmark excels in three key areas: \textbf{a) Long-Duration, Information-Dense Videos:} We carefully select approximately 1,000 videos from open-source datasets FineVideo based on duration limit and the information density of both visual and audio modalities, focusing on content like lectures, interviews, and vlogs, which contain rich language elements. \textbf{b) Diverse and Challenging Task Scenarios:} We have designed six challenging task scenarios, including both Intra-Event and Inter-Event Tasks. \textbf{c) Rigorous and Comprehensive Quality Assurance Pipelines:} We have developed a three-step, semi-automated data quality assurance pipeline to ensure the difficulty and validity of the synthesized questions and answer options. Based on LongInsightBench, we designed a series of experiments. Experimental results shows that Omni-modal models(OLMs) still face challenge in tasks requiring precise temporal localization (T-Loc) and long-range causal inference (CE-Caus). Extended experiments reveal the information loss and processing bias in multi-modal fusion of OLMs. Our dataset and code is available at https://anonymous.4open.science/r/LongInsightBench-910F/.
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            <a href="https://www.alphaxiv.org/abs/2510.17299v1" target="_blank" rel="noopener noreferrer">
                探索自监督学习中稠密表示的结构性退化
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Exploring Structural Degradation in Dense Representations for Self-supervised Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Siran Dai, Qianqian Xu, Peisong Wen, Yang Liu, Qingming Huang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究自监督学习中表示质量的结构性退化问题，属于表示学习的基础研究。虽然稠密表示在推荐和搜索系统中广泛使用，但该工作聚焦于自监督学习的表示质量退化机制，与推荐系统、搜索或广告的直接应用关联较弱，且未明确涉及LLM或Transformer架构的进展。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 08:40:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17299v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17299v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    In this work, we observe a counterintuitive phenomenon in self-supervised learning (SSL): longer training may impair the performance of dense prediction tasks (e.g., semantic segmentation). We refer to this phenomenon as Self-supervised Dense Degradation (SDD) and demonstrate its consistent presence across sixteen state-of-the-art SSL methods with various losses, architectures, and datasets. When the model performs suboptimally on dense tasks at the end of training, measuring the performance during training becomes essential. However, evaluating dense performance effectively without annotations remains an open challenge. To tackle this issue, we introduce a Dense representation Structure Estimator (DSE), composed of a class-relevance measure and an effective dimensionality measure. The proposed DSE is both theoretically grounded and empirically validated to be closely correlated with the downstream performance. Based on this metric, we introduce a straightforward yet effective model selection strategy and a DSE-based regularization method. Experiments on sixteen SSL methods across four benchmarks confirm that model selection improves mIoU by $3.0\%$ on average with negligible computational cost. Additionally, DSE regularization consistently mitigates the effects of dense degradation. Code is available at https://github.com/EldercatSAM/SSL-Degradation.
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            <a href="https://www.alphaxiv.org/abs/2510.17269v1" target="_blank" rel="noopener noreferrer">
                FineVision：开放数据即所需全部
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            FineVision: Open Data Is All You Need
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Luis Wiedmann, Orr Zohar, Amir Mahla, Xiaohan Wang, Rui Li, Thibaud Frere, Leand...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该标题暗示了视觉领域的模型微调或数据利用方法，但未明确涉及推荐系统、搜索或广告的核心技术。虽然标题中的'Open Data'概念可能间接关联到数据增强，但缺乏对Transformer架构、LLM技术或异构数据统一建模的具体指向，与当前关注点的直接关联性较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 07:54:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17269v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17269v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The advancement of vision-language models (VLMs) is hampered by a fragmented landscape of inconsistent and contaminated public datasets. We introduce FineVision, a meticulously collected, curated, and unified corpus of 24 million samples - the largest open resource of its kind. We unify more than 200 sources into 185 subsets via a semi-automated, human-in-the-loop pipeline: automation performs bulk ingestion and schema mapping, while reviewers audit mappings and spot-check outputs to verify faithful consumption of annotations, appropriate formatting and diversity, and safety; issues trigger targeted fixes and re-runs. The workflow further applies rigorous de-duplication within and across sources and decontamination against 66 public benchmarks. FineVision also encompasses agentic/GUI tasks with a unified action space; reviewers validate schemas and inspect a sample of trajectories to confirm executable fidelity. Models trained on FineVision consistently outperform those trained on existing open mixtures across a broad evaluation suite, underscoring the benefits of scale, data hygiene, and balanced automation with human oversight. We release the corpus and curation tools to accelerate data-centric VLM research.
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            <a href="https://www.alphaxiv.org/abs/2510.17234v1" target="_blank" rel="noopener noreferrer">
                持续音频-视觉分割中的模态纠缠驯服
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Taming Modality Entanglement in Continual Audio-Visual Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuyang Hong, Qi Yang, Tao Zhang, Zili Wang, Zhaojin Fu, Kun Ding, Bin Fan, Shimi...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音频-视觉分割的持续学习，属于多模态学习范畴，但与推荐系统、搜索或广告的核心技术领域关联较弱。虽然模态纠缠处理技术可能对处理异构数据有一定启发，但音频-视觉分割本身在RecSys/Search/Ads中的直接应用场景有限，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 07:23:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17234v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17234v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.MM</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recently, significant progress has been made in multi-modal continual learning, aiming to learn new tasks sequentially in multi-modal settings while preserving performance on previously learned ones. However, existing methods mainly focus on coarse-grained tasks, with limitations in addressing modality entanglement in fine-grained continual learning settings. To bridge this gap, we introduce a novel Continual Audio-Visual Segmentation (CAVS) task, aiming to continuously segment new classes guided by audio. Through comprehensive analysis, two critical challenges are identified: 1) multi-modal semantic drift, where a sounding objects is labeled as background in sequential tasks; 2) co-occurrence confusion, where frequent co-occurring classes tend to be confused. In this work, a Collision-based Multi-modal Rehearsal (CMR) framework is designed to address these challenges. Specifically, for multi-modal semantic drift, a Multi-modal Sample Selection (MSS) strategy is proposed to select samples with high modal consistency for rehearsal. Meanwhile, for co-occurence confusion, a Collision-based Sample Rehearsal (CSR) mechanism is designed, allowing for the increase of rehearsal sample frequency of those confusable classes during training process. Moreover, we construct three audio-visual incremental scenarios to verify effectiveness of our method. Comprehensive experiments demonstrate that our method significantly outperforms single-modal continual learning methods.
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            <a href="https://www.alphaxiv.org/abs/2510.17188v1" target="_blank" rel="noopener noreferrer">
                HIDISC：一种用于领域泛化与广义类别发现的双曲框架
            </a>
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        <div class="mb-2 text-base text-gray-700">
            HIDISC: A Hyperbolic Framework for Domain Generalization with Generalized Category Discovery
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Vaibhav Rathore, Divyam Gupta, Biplab Banerjee
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注领域泛化和类别发现，属于通用机器学习范畴，与推荐系统、搜索或广告的核心技术关联较弱。虽然双曲几何在表示学习中有应用，但论文未明确展示在RecSys/Search/Ads中的具体应用潜力，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 06:08:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17188v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17188v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Generalized Category Discovery (GCD) aims to classify test-time samples into either seen categories** -- available during training -- or novel ones, without relying on label supervision. Most existing GCD methods assume simultaneous access to labeled and unlabeled data during training and arising from the same domain, limiting applicability in open-world scenarios involving distribution shifts. Domain Generalization with GCD (DG-GCD) lifts this constraint by requiring models to generalize to unseen domains containing novel categories, without accessing targetdomain data during training. The only prior DG-GCD method, DG2CD-Net, relies on episodic training with multiple synthetic domains and task vector aggregation, incurring high computational cost and error accumulation. We propose HIDISC, a hyperbolic representation learning framework that achieves domain and category-level generalization without episodic simulation. To expose the model to minimal but diverse domain variations, we augment the source domain using GPT-guided diffusion, avoiding overfitting while maintaining efficiency. To structure the representation space, we introduce Tangent CutMix, a curvature-aware interpolation that synthesizes pseudo-novel samples in tangent space, preserving manifold consistency. A unified loss -- combining penalized Busemann alignment, hybrid hyperbolic contrastive regularization, and adaptive outlier repulsion -- **facilitates compact, semantically structured embeddings. A learnable curvature parameter further adapts the geometry to dataset complexity. HIDISC achieves state-of-the-art results on PACS , Office-Home , and DomainNet, consistently outperforming the existing Euclidean and hyperbolic (DG)-GCD baselines.
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            <a href="https://www.alphaxiv.org/abs/2510.17137v1" target="_blank" rel="noopener noreferrer">
                KineDiff3D：面向类别级铰接物体形状重建与生成的运动感知扩散模型
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        <div class="mb-2 text-base text-gray-700">
            KineDiff3D: Kinematic-Aware Diffusion for Category-Level Articulated Object Shape Reconstruction and Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>WenBo Xu, Liu Liu, Li Zhang, Ran Zhang, Hao Wu, Dan Guo, Meng Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D铰接物体的形状重建与生成，属于计算机视觉和3D几何处理领域。虽然扩散模型是LLM相关的生成技术，但该工作主要应用于物理对象建模，与推荐系统、搜索或广告的核心技术栈缺乏直接关联。其运动感知特性可能在理解用户与物理产品交互方面有间接启发，但应用场景过于狭窄且不明确。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 04:15:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17137v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17137v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Articulated objects, such as laptops and drawers, exhibit significant challenges for 3D reconstruction and pose estimation due to their multi-part geometries and variable joint configurations, which introduce structural diversity across different states. To address these challenges, we propose KineDiff3D: Kinematic-Aware Diffusion for Category-Level Articulated Object Shape Reconstruction and Generation, a unified framework for reconstructing diverse articulated instances and pose estimation from single view input. Specifically, we first encode complete geometry (SDFs), joint angles, and part segmentation into a structured latent space via a novel Kinematic-Aware VAE (KA-VAE). In addition, we employ two conditional diffusion models: one for regressing global pose (SE(3)) and joint parameters, and another for generating the kinematic-aware latent code from partial observations. Finally, we produce an iterative optimization module that bidirectionally refines reconstruction accuracy and kinematic parameters via Chamfer-distance minimization while preserving articulation constraints. Experimental results on synthetic, semi-synthetic, and real-world datasets demonstrate the effectiveness of our approach in accurately reconstructing articulated objects and estimating their kinematic properties.
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            <a href="https://www.alphaxiv.org/abs/2510.17120v1" target="_blank" rel="noopener noreferrer">
                矩阵自由能作为高斯化正则化器：增强自编码器的高斯码生成能力
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        <div class="mb-2 text-base text-gray-700">
            Matricial Free Energy as a Gaussianizing Regularizer: Enhancing Autoencoders for Gaussian Code Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Rishi Sonthalia, Raj Rao Nadakuditi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注自编码器的高斯码生成和正则化技术，属于通用的深度学习优化方法。虽然正则化技术可能对模型训练有普遍价值，但论文没有明确展示与推荐系统、搜索或广告的具体应用连接，也没有涉及Transformer架构改进或LLM技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 03:19:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17120v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17120v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span><span class="category-tag">stat.ML</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce a novel regularization scheme for autoencoders based on matricial free energy. Our approach defines a differentiable loss function in terms of the singular values of the code matrix (code dimension x batch size). From the standpoint of free probability an d random matrix theory, this loss achieves its minimum when the singular value distribution of the code matrix coincides with that of an appropriately sculpted random metric with i.i.d. Gaussian entries. Empirical simulations demonstrate that minimizing the negative matricial free energy through standard stochastic gradient-based training yields Gaussian-like codes that generalize across training and test sets. Building on this foundation, we propose a matricidal free energy maximizing autoencoder that reliably produces Gaussian codes and show its application to underdetermined inverse problems.
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            <a href="https://www.alphaxiv.org/abs/2510.17101v1" target="_blank" rel="noopener noreferrer">
                形状感知惯性姿态估计器：使用稀疏惯性传感器对多样化体型人体进行运动追踪
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            Shape-aware Inertial Poser: Motion Tracking for Humans with Diverse Shapes Using Sparse Inertial Sensors
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lu Yin, Ziying Shi, Yinghao Wu, Xinyu Yi, Feng Xu, Shihui Guo
        </div>
        
        
        
        
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于人体运动追踪和计算机视觉领域，使用惯性传感器进行姿态估计。虽然涉及传感器数据处理，但其核心应用场景是人体动作捕捉和动画生成，与推荐系统、搜索或广告的核心技术栈没有直接关联。该技术缺乏明确的路径应用于用户行为建模或内容理解等RecSys/Search/Ads领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 02:20:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17101v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17101v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.GR</span><span class="category-tag">cs.CV</span></div>
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                    Human motion capture with sparse inertial sensors has gained significant attention recently. However, existing methods almost exclusively rely on a template adult body shape to model the training data, which poses challenges when generalizing to individuals with largely different body shapes (such as a child). This is primarily due to the variation in IMU-measured acceleration caused by changes in body shape. To fill this gap, we propose Shape-aware Inertial Poser (SAIP), the first solution considering body shape differences in sparse inertial-based motion capture. Specifically, we decompose the sensor measurements related to shape and pose in order to effectively model their joint correlations. Firstly, we train a regression model to transfer the IMU-measured accelerations of a real body to match the template adult body model, compensating for the shape-related sensor measurements. Then, we can easily follow the state-of-the-art methods to estimate the full body motions of the template-shaped body. Finally, we utilize a second regression model to map the joint velocities back to the real body, combined with a shape-aware physical optimization strategy to calculate global motions on the subject. Furthermore, our method relies on body shape awareness, introducing the first inertial shape estimation scheme. This is accomplished by modeling the shape-conditioned IMU-pose correlation using an MLP-based network. To validate the effectiveness of SAIP, we also present the first IMU motion capture dataset containing individuals of different body sizes. This dataset features 10 children and 10 adults, with heights ranging from 110 cm to 190 cm, and a total of 400 minutes of paired IMU-Motion samples. Extensive experimental results demonstrate that SAIP can effectively handle motion capture tasks for diverse body shapes. The code and dataset are available at https://github.com/yinlu5942/SAIP.
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            <a href="https://www.alphaxiv.org/abs/2510.17078v1" target="_blank" rel="noopener noreferrer">
                面向多模态目标检测的通用化融合架构研究
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            Towards a Generalizable Fusion Architecture for Multimodal Object Detection
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jad Berjawi, Yoann Dupas, Christophe C'erin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于多模态目标检测，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术关联度较低。虽然多模态融合技术在某些边缘场景下可能为推荐系统提供视觉特征增强，但这种应用过于间接且非核心关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 01:19:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17078v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17078v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">I.2.10; I.4.8</span></div>
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                    Multimodal object detection improves robustness in chal- lenging conditions by leveraging complementary cues from multiple sensor modalities. We introduce Filtered Multi- Modal Cross Attention Fusion (FMCAF), a preprocess- ing architecture designed to enhance the fusion of RGB and infrared (IR) inputs. FMCAF combines a frequency- domain filtering block (Freq-Filter) to suppress redun- dant spectral features with a cross-attention-based fusion module (MCAF) to improve intermodal feature sharing. Unlike approaches tailored to specific datasets, FMCAF aims for generalizability, improving performance across different multimodal challenges without requiring dataset- specific tuning. On LLVIP (low-light pedestrian detec- tion) and VEDAI (aerial vehicle detection), FMCAF outper- forms traditional fusion (concatenation), achieving +13.9% mAP@50 on VEDAI and +1.1% on LLVIP. These results support the potential of FMCAF as a flexible foundation for robust multimodal fusion in future detection pipelines.
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            <a href="https://www.alphaxiv.org/abs/2510.17764v1" target="_blank" rel="noopener noreferrer">
                基于自主性级别评估医学大语言模型：从基准测试迈向应用的综述
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        <div class="mb-2 text-base text-gray-700">
            Evaluating Medical LLMs by Levels of Autonomy: A Survey Moving from Benchmarks to Applications
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiao Ye, Jacob Dineen, Zhaonan Li, Zhikun Xu, Weiyu Chen, Shijie Lu, Yuxi Huang,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的LLM评估和应用，属于明确的无关主题（医学领域应用）。虽然涉及LLM评估，但这是纯粹的医学应用场景，与推荐系统、搜索或广告领域没有任何关联。论文内容完全偏离了当前关注的技术领域和应用方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:22:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17764v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17764v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Medical Large language models achieve strong scores on standard benchmarks; however, the transfer of those results to safe and reliable performance in clinical workflows remains a challenge. This survey reframes evaluation through a levels-of-autonomy lens (L0-L3), spanning informational tools, information transformation and aggregation, decision support, and supervised agents. We align existing benchmarks and metrics with the actions permitted at each level and their associated risks, making the evaluation targets explicit. This motivates a level-conditioned blueprint for selecting metrics, assembling evidence, and reporting claims, alongside directions that link evaluation to oversight. By centering autonomy, the survey moves the field beyond score-based claims toward credible, risk-aware evidence for real clinical use.
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            <a href="https://www.alphaxiv.org/abs/2510.17759v1" target="_blank" rel="noopener noreferrer">
                VERA-V：用于越狱视觉语言模型的变分推理框架
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            VERA-V: Variational Inference Framework for Jailbreaking Vision-Language Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qilin Liao, Anamika Lochab, Ruqi Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视觉语言模型的安全性和对抗性攻击（越狱），这属于安全领域，被明确列为不相关主题。虽然提到了视觉语言模型，但核心关注点是安全漏洞而非推荐系统、搜索或广告的应用。该工作没有展示在推荐系统、搜索或广告中的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:12:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17759v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17759v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CR</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Vision-Language Models (VLMs) extend large language models with visual reasoning, but their multimodal design also introduces new, underexplored vulnerabilities. Existing multimodal red-teaming methods largely rely on brittle templates, focus on single-attack settings, and expose only a narrow subset of vulnerabilities. To address these limitations, we introduce VERA-V, a variational inference framework that recasts multimodal jailbreak discovery as learning a joint posterior distribution over paired text-image prompts. This probabilistic view enables the generation of stealthy, coupled adversarial inputs that bypass model guardrails. We train a lightweight attacker to approximate the posterior, allowing efficient sampling of diverse jailbreaks and providing distributional insights into vulnerabilities. VERA-V further integrates three complementary strategies: (i) typography-based text prompts that embed harmful cues, (ii) diffusion-based image synthesis that introduces adversarial signals, and (iii) structured distractors to fragment VLM attention. Experiments on HarmBench and HADES benchmarks show that VERA-V consistently outperforms state-of-the-art baselines on both open-source and frontier VLMs, achieving up to 53.75% higher attack success rate (ASR) over the best baseline on GPT-4o.
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            <a href="https://www.alphaxiv.org/abs/2510.17662v1" target="_blank" rel="noopener noreferrer">
                DELULU：基于潜在单元的判别性嵌入学习用于说话人感知的自监督语音基础模型
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            DELULU: Discriminative Embedding Learning Using Latent Units for Speaker-Aware Self-Supervised Speech Foundational Model
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Massa Baali, Rita Singh, Bhiksha Raj
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音领域的自监督学习模型，属于语音处理技术范畴。虽然涉及基础模型和嵌入学习，但论文明确针对语音信号和说话人识别，与推荐系统、搜索或广告的核心技术领域没有直接关联，也不符合视觉语言模型对异构数据处理的类比要求。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:35:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17662v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17662v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SD</span><span class="category-tag">cs.CL</span></div>
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                    Self-supervised speech models have achieved remarkable success on content-driven tasks, yet they remain limited in capturing speaker-discriminative features critical for verification, diarization, and profiling applications. We introduce DELULU, a speaker-aware self-supervised foundational model that addresses this limitation by integrating external supervision into the pseudo-label generation process. DELULU leverages frame-level embeddings from ReDimNet, a state-of-the-art speaker verification model, to guide the k-means clustering step during pre-training, introducing a strong speaker-discriminative inductive bias that aligns representation learning with speaker identity. The model is trained using a dual objective that combines masked prediction and denoising, further enhancing robustness and generalization. DELULU significantly outperforms prior self-supervised learning (SSL) models across a range of speaker-centric tasks, achieving up to 62% relative improvement in equal error rate (EER) for speaker verification and consistent gains on zero-shot profiling tasks such as gender, age, accent, and speaker counting. Our findings demonstrate that DELULU is a strong universal encoder for speaker-aware speech processing, enabling superior performance even without task-specific fine-tuning.
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                LawChain：面向中国侵权案件分析的法律推理链建模
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            LawChain: Modeling Legal Reasoning Chains for Chinese Tort Case Analysis
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Huiyuan Xie, Chenyang Li, Huining Zhu, Chubin Zhang, Yuxiao Ye, Zhenghao Liu, Zh...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于法律领域的特定应用（侵权案件分析），属于法律科技范畴，与推荐系统、搜索或广告的核心技术领域没有直接关联。论文内容涉及法律推理链建模，这在技术路径和应用场景上都与我的关注焦点相距甚远。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:50:58
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                <a href="https://arxiv.org/abs/2510.17602v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17602v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Legal reasoning is a fundamental component of legal analysis and decision-making. Existing computational approaches to legal reasoning predominantly rely on generic reasoning frameworks such as syllogism and IRAC, which do not comprehensively examine the nuanced processes that underpin legal reasoning. Moreover, current research has largely focused on criminal cases, with insufficient modeling for civil cases. In this work, we present a novel framework for explicitly modeling legal reasoning in the analysis of Chinese tort-related civil cases. We first operationalize the legal reasoning processes used in tort analysis into the LawChain framework. LawChain is a three-module reasoning framework, with each module consisting of multiple finer-grained sub-steps. Informed by the LawChain framework, we introduce the task of tort legal reasoning and construct an evaluation benchmark, LawChain$_{eval}$, to systematically assess the critical steps within analytical reasoning chains for tort analysis. Leveraging this benchmark, we evaluate state-of-the-art large language models for their legal reasoning ability in civil tort contexts. Our results indicate that current models still fall short in accurately handling crucial elements of tort legal reasoning. Furthermore, we introduce several baseline approaches that explicitly incorporate LawChain-style reasoning through prompting or post-training. We conduct further experiments on additional legal analysis tasks, such as Legal Named-Entity Recognition and Criminal Damages Calculation, to verify the generalizability of these baselines. The proposed baseline approaches achieve significant improvements in tort-related legal reasoning and generalize well to related legal analysis tasks, thus demonstrating the value of explicitly modeling legal reasoning chains to enhance the reasoning capabilities of language models.
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            <a href="https://www.alphaxiv.org/abs/2510.17532v1" target="_blank" rel="noopener noreferrer">
                OncoReason：在大型语言模型中结构化临床推理以实现稳健且可解释的生存预测
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            OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai, Anna Ewa Choromans...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的生存预测应用，属于明确的医疗领域特定应用，与搜索、推荐或广告系统完全无关。论文标题明确指向临床推理和肿瘤学预测，这完全属于被排除的医疗/生物学应用范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 13:35:12
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                <a href="https://arxiv.org/abs/2510.17532v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17532v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data. While large language models (LLMs) have shown strong performance in biomedical NLP, they often lack structured reasoning capabilities critical for high-stakes decision support. We present a unified, multi-task learning framework that aligns autoregressive LLMs with clinical reasoning for outcome prediction on the MSK-CHORD dataset. Our models are trained to jointly perform binary survival classification, continuous survival time regression, and natural language rationale generation. We evaluate three alignment strategies: (1) standard supervised fine-tuning (SFT), (2) SFT with Chain-of-Thought (CoT) prompting to elicit step-by-step reasoning, and (3) Group Relative Policy Optimization (GRPO), a reinforcement learning method that aligns model outputs to expert-derived reasoning trajectories. Experiments with LLaMa3-8B and Med42-8B backbones demonstrate that CoT prompting improves F1 by +6.0 and reduces MAE by 12%, while GRPO achieves state-of-the-art interpretability and predictive performance across BLEU, ROUGE, and BERTScore. We further show that existing biomedical LLMs often fail to produce valid reasoning traces due to architectural constraints. Our findings underscore the importance of reasoning-aware alignment in multi-task clinical modeling and set a new benchmark for interpretable, trustworthy LLMs in precision oncology.
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            <a href="https://www.alphaxiv.org/abs/2510.17516v1" target="_blank" rel="noopener noreferrer">
                SimBench：评估大型语言模型模拟人类行为能力的基准
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            SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tiancheng Hu, Joachim Baumann, Lorenzo Lupo, Dirk Hovy, Nigel Collier, Paul Rött...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM评估基准和模拟人类行为的能力评估，这属于纯粹的NLP评估基准范畴，与推荐系统、搜索或广告的核心技术无关。论文标题明确表明其关注点是基准测试和模拟能力评估，而非任何推荐、搜索或广告领域的实际应用或技术进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 13:14:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17516v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17516v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CY</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language model (LLM) simulations of human behavior have the potential to revolutionize the social and behavioral sciences, if and only if they faithfully reflect real human behaviors. Current evaluations are fragmented, based on bespoke tasks and metrics, creating a patchwork of incomparable results. To address this, we introduce SimBench, the first large-scale, standardized benchmark for a robust, reproducible science of LLM simulation. By unifying 20 diverse datasets covering tasks from moral decision-making to economic choice across a large global participant pool, SimBench provides the necessary foundation to ask fundamental questions about when, how, and why LLM simulations succeed or fail. We show that, while even the best LLMs today have limited simulation ability (score: 40.80/100), performance scales log-linearly with model size. Simulation performance is not improved by increased inference-time compute. We demonstrate an alignment-simulation trade-off: instruction-tuning improves performance on low-entropy (consensus) questions but degrades it on high-entropy (diverse) ones. Models particularly struggle when simulating specific demographic groups. Finally, we demonstrate that simulation ability correlates most strongly with deep, knowledge-intensive reasoning (MMLU-Pro, r=0.939). By making progress measurable, we aim to accelerate the development of more faithful LLM simulators.
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            <a href="https://www.alphaxiv.org/abs/2510.17504v1" target="_blank" rel="noopener noreferrer">
                Lingua Custodi 参与 WMT 2025 术语共享任务
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            Lingua Custodi's participation at the WMT 2025 Terminology shared task
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingshu Liu, Raheel Qader, Gaëtan Caillaut, Mariam Nakhlé
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于机器翻译领域的术语共享任务，属于纯NLP应用场景。虽然涉及多语言处理，但未体现与推荐系统、搜索或广告相关的核心进展、LLM技术应用或Transformer架构创新，也未展示对异构数据的统一建模方法。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 13:00:47
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17504v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17504v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embeddings by combining the best methods for learning monolingual and cross-lingual representations including: masked language modeling (MLM), translation language modeling (TLM), dual encoder translation ranking, and additive margin softmax. We show that introducing a pre-trained multilingual language model dramatically reduces the amount of parallel training data required to achieve good performance by 80%. Composing the best of these methods produces a model that achieves 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba, well above the 65.5 achieved by LASER, while still performing competitively on monolingual transfer learning benchmarks. Parallel data mined from CommonCrawl using our best model is shown to train competitive NMT models for en-zh and en-de. We publicly release our best multilingual sentence embedding model for 109+ languages at https://tfhub.dev/google/LaBSE.
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            <a href="https://www.alphaxiv.org/abs/2510.17476v1" target="_blank" rel="noopener noreferrer">
                基于多语言大语言模型的医疗问答中的差异性
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            Disparities in Multilingual LLM-Based Healthcare Q&A
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ipek Baris Schlicht, Burcu Sayin, Zhixue Zhao, Frederik M. Labonté, Cesare Barbe...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医疗领域的问答系统，这属于明确的无关主题（Medical domain-specific applications）。虽然涉及LLM技术，但应用场景与推荐系统、搜索或广告完全无关，且论文关注的是差异性分析而非技术架构或应用创新。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 12:19:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17476v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17476v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Equitable access to reliable health information is vital when integrating AI into healthcare. Yet, information quality varies across languages, raising concerns about the reliability and consistency of multilingual Large Language Models (LLMs). We systematically examine cross-lingual disparities in pre-training source and factuality alignment in LLM answers for multilingual healthcare Q&A across English, German, Turkish, Chinese (Mandarin), and Italian. We (i) constructed Multilingual Wiki Health Care (MultiWikiHealthCare), a multilingual dataset from Wikipedia; (ii) analyzed cross-lingual healthcare coverage; (iii) assessed LLM response alignment with these references; and (iv) conducted a case study on factual alignment through the use of contextual information and Retrieval-Augmented Generation (RAG). Our findings reveal substantial cross-lingual disparities in both Wikipedia coverage and LLM factual alignment. Across LLMs, responses align more with English Wikipedia, even when the prompts are non-English. Providing contextual excerpts from non-English Wikipedia at inference time effectively shifts factual alignment toward culturally relevant knowledge. These results highlight practical pathways for building more equitable, multilingual AI systems for healthcare.
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                评估大型语言模型在乌尔都语习语翻译上的表现
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            Evaluating Large Language Models on Urdu Idiom Translation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Muhammad Farmal Khan, Mousumi Akter
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于乌尔都语习语翻译的评估，这属于特定语言的NLP评估任务，与推荐系统、搜索或广告的核心技术进展无关。论文内容涉及LLM的评估基准和特定语言能力测试，属于纯粹的NLP评估范畴，没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 11:49:26
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                <a href="https://arxiv.org/abs/2510.17460v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17460v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Idiomatic translation remains a significant challenge in machine translation, especially for low resource languages such as Urdu, and has received limited prior attention. To advance research in this area, we introduce the first evaluation datasets for Urdu to English idiomatic translation, covering both Native Urdu and Roman Urdu scripts and annotated with gold-standard English equivalents. We evaluate multiple open-source Large Language Models (LLMs) and Neural Machine Translation (NMT) systems on this task, focusing on their ability to preserve idiomatic and cultural meaning. Automatic metrics including BLEU, BERTScore, COMET, and XCOMET are used to assess translation quality. Our findings indicate that prompt engineering enhances idiomatic translation compared to direct translation, though performance differences among prompt types are relatively minor. Moreover, cross script comparisons reveal that text representation substantially affects translation quality, with Native Urdu inputs producing more accurate idiomatic translations than Roman Urdu.
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            <a href="https://www.alphaxiv.org/abs/2510.17437v1" target="_blank" rel="noopener noreferrer">
                基于BERT嵌入的多语言临床命名实体识别：用于心内科文本中疾病与药物识别
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            Multilingual Clinical NER for Diseases and Medications Recognition in Cardiology Texts using BERT Embeddings
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Manuela Daniela Danu, George Marica, Constantin Suciu, Lucian Mihai Itu, Oladime...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的命名实体识别，属于医学/生物学应用范畴，与推荐系统、搜索或广告的核心技术无关。BERT嵌入技术虽然相关，但论文的应用场景（心内科临床文本）完全在排除的医疗领域内，没有任何与RecSys/Search/Ads相关的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 11:26:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17437v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17437v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The rapidly increasing volume of electronic health record (EHR) data underscores a pressing need to unlock biomedical knowledge from unstructured clinical texts to support advancements in data-driven clinical systems, including patient diagnosis, disease progression monitoring, treatment effects assessment, prediction of future clinical events, etc. While contextualized language models have demonstrated impressive performance improvements for named entity recognition (NER) systems in English corpora, there remains a scarcity of research focused on clinical texts in low-resource languages. To bridge this gap, our study aims to develop multiple deep contextual embedding models to enhance clinical NER in the cardiology domain, as part of the BioASQ MultiCardioNER shared task. We explore the effectiveness of different monolingual and multilingual BERT-based models, trained on general domain text, for extracting disease and medication mentions from clinical case reports written in English, Spanish, and Italian. We achieved an F1-score of 77.88% on Spanish Diseases Recognition (SDR), 92.09% on Spanish Medications Recognition (SMR), 91.74% on English Medications Recognition (EMR), and 88.9% on Italian Medications Recognition (IMR). These results outperform the mean and median F1 scores in the test leaderboard across all subtasks, with the mean/median values being: 69.61%/75.66% for SDR, 81.22%/90.18% for SMR, 89.2%/88.96% for EMR, and 82.8%/87.76% for IMR.
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            <a href="https://www.alphaxiv.org/abs/2510.17415v1" target="_blank" rel="noopener noreferrer">
                本草：一个针对中医药领域进行指令微调的大语言模型
            </a>
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        <div class="mb-2 text-base text-gray-700">
            BenCao: An Instruction-Tuned Large Language Model for Traditional Chinese Medicine
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiacheng Xie, Yang Yu, Yibo Chen, Hanyao Zhang, Lening Zhao, Jiaxuan He, Lei Jia...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于中医药领域的领域特定应用，这属于明确的无关主题范畴（医学/生物学领域特定应用）。虽然涉及指令微调技术，但该技术被应用于与推荐系统、搜索或广告完全无关的特定垂直领域，没有任何潜在的应用相关性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:57:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17415v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17415v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.MA</span><span class="category-tag">cs.MM</span><span class="category-tag">cs.SE</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Traditional Chinese Medicine (TCM), with a history spanning over two millennia, plays a role in global healthcare. However, applying large language models (LLMs) to TCM remains challenging due to its reliance on holistic reasoning, implicit logic, and multimodal diagnostic cues. Existing TCM-domain LLMs have made progress in text-based understanding but lack multimodal integration, interpretability, and clinical applicability. To address these limitations, we developed BenCao, a ChatGPT-based multimodal assistant for TCM, integrating structured knowledge bases, diagnostic data, and expert feedback refinement. BenCao was trained through natural language instruction tuning rather than parameter retraining, aligning with expert-level reasoning and ethical norms specific to TCM. The system incorporates a comprehensive knowledge base of over 1,000 classical and modern texts, a scenario-based instruction framework for diverse interactions, a chain-of-thought simulation mechanism for interpretable reasoning, and a feedback refinement process involving licensed TCM practitioners. BenCao connects to external APIs for tongue-image classification and multimodal database retrieval, enabling dynamic access to diagnostic resources. In evaluations across single-choice question benchmarks and multimodal classification tasks, BenCao achieved superior accuracy to general-domain and TCM-domain models, particularly in diagnostics, herb recognition, and constitution classification. The model was deployed as an interactive application on the OpenAI GPTs Store, accessed by nearly 1,000 users globally as of October 2025. This study demonstrates the feasibility of developing a TCM-domain LLM through natural language-based instruction tuning and multimodal integration, offering a practical framework for aligning generative AI with traditional medical reasoning and a scalable pathway for real-world deployment.
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            <a href="https://www.alphaxiv.org/abs/2510.17405v1" target="_blank" rel="noopener noreferrer">
                AFRICAPTION：为非洲语言图像描述建立新范式
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            AFRICAPTION: Establishing a New Paradigm for Image Captioning in African Languages
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mardiyyah Oduwole, Prince Mireku, Fatimo Adebanjo, Oluwatosin Olajide, Mahi Amin...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于非洲语言的图像描述生成，这属于纯粹的视觉-语言多模态任务，与推荐系统、搜索或广告的核心技术进展无关。虽然提到了多模态建模，但其应用场景和语言焦点与当前关注的异构数据统一建模或LLM在推荐/搜索中的应用没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:44:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17405v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17405v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Multimodal AI research has overwhelmingly focused on high-resource languages, hindering the democratization of advancements in the field. To address this, we present AfriCaption, a comprehensive framework for multilingual image captioning in 20 African languages and our contributions are threefold: (i) a curated dataset built on Flickr8k, featuring semantically aligned captions generated via a context-aware selection and translation process; (ii) a dynamic, context-preserving pipeline that ensures ongoing quality through model ensembling and adaptive substitution; and (iii) the AfriCaption model, a 0.5B parameter vision-to-text architecture that integrates SigLIP and NLLB200 for caption generation across under-represented languages. This unified framework ensures ongoing data quality and establishes the first scalable image-captioning resource for under-represented African languages, laying the groundwork for truly inclusive multimodal AI.
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            <a href="https://www.alphaxiv.org/abs/2510.17402v1" target="_blank" rel="noopener noreferrer">
                利用群体相对策略优化推进大型语言模型在传统中医领域的应用
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            Leveraging Group Relative Policy Optimization to Advance Large Language Models in Traditional Chinese Medicine
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiacheng Xie, Shuai Zeng, Yang Yu, Xiaoting Tang, Guanghui An, Dong Xu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于传统中医这一特定领域应用，属于明确的无关主题范畴。虽然涉及LLM技术，但其应用场景与搜索、推荐、广告等核心领域完全无关，且没有展示任何在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:43:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17402v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17402v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Traditional Chinese Medicine (TCM) presents a rich and structurally unique knowledge system that challenges conventional applications of large language models (LLMs). Although previous TCM-specific LLMs have shown progress through supervised fine-tuning, they often face limitations in alignment, data quality, and evaluation consistency. In this study, we introduce Ladder-base, the first TCM-focused LLM trained with Group Relative Policy Optimization (GRPO), a reinforcement learning method that improves reasoning and factual consistency by optimizing response selection based on intra-group comparisons. Ladder-base is built upon the Qwen2.5-7B-Instruct foundation model and trained exclusively on the textual subset of the TCM-Ladder benchmark, using 80 percent of the data for training and the remaining 20 percent split evenly between validation and test sets. Through standardized evaluation, Ladder-base demonstrates superior performance across multiple reasoning metrics when compared to both state-of-the-art general-purpose LLMs such as GPT-4, Gemini 2.5, Claude 3, and Qwen3 and domain-specific TCM models including BenTsao, HuatuoGPT2, and Zhongjing. These findings suggest that GRPO provides an effective and efficient strategy for aligning LLMs with expert-level reasoning in traditional medical domains and supports the development of trustworthy and clinically grounded TCM artificial intelligence systems.
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            <a href="https://www.alphaxiv.org/abs/2510.17247v1" target="_blank" rel="noopener noreferrer">
                从偏好到偏见：对齐调优在视频扩散模型中塑造社会偏见的作用
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            From Preferences to Prejudice: The Role of Alignment Tuning in Shaping Social Bias in Video Diffusion Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zefan Cai, Haoyi Qiu, Haozhe Zhao, Ke Wan, Jiachen Li, Jiuxiang Gu, Wen Xiao, Na...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究视频扩散模型中的社会偏见问题，这属于伦理和公平性范畴，属于明确排除的无关主题。论文内容涉及模型偏见评估，属于非技术性话题，与推荐系统、搜索或广告的核心技术进展没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 07:37:43
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                <a href="https://arxiv.org/abs/2510.17247v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17247v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
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                    Recent advances in video diffusion models have significantly enhanced text-to-video generation, particularly through alignment tuning using reward models trained on human preferences. While these methods improve visual quality, they can unintentionally encode and amplify social biases. To systematically trace how such biases evolve throughout the alignment pipeline, we introduce VideoBiasEval, a comprehensive diagnostic framework for evaluating social representation in video generation. Grounded in established social bias taxonomies, VideoBiasEval employs an event-based prompting strategy to disentangle semantic content (actions and contexts) from actor attributes (gender and ethnicity). It further introduces multi-granular metrics to evaluate (1) overall ethnicity bias, (2) gender bias conditioned on ethnicity, (3) distributional shifts in social attributes across model variants, and (4) the temporal persistence of bias within videos. Using this framework, we conduct the first end-to-end analysis connecting biases in human preference datasets, their amplification in reward models, and their propagation through alignment-tuned video diffusion models. Our results reveal that alignment tuning not only strengthens representational biases but also makes them temporally stable, producing smoother yet more stereotyped portrayals. These findings highlight the need for bias-aware evaluation and mitigation throughout the alignment process to ensure fair and socially responsible video generation.
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            <a href="https://www.alphaxiv.org/abs/2510.17173v1" target="_blank" rel="noopener noreferrer">
                基于真实用户的LLM多轮健康指导离线策略评估
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            Offline Policy Evaluation of Multi-Turn LLM Health Coaching with Real Users
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Melik Ozolcer, Sang Won Bae
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确聚焦于医疗健康领域的LLM应用，这属于明确的无关主题范畴。虽然涉及LLM和多轮对话，但其医疗健康应用场景与搜索、推荐、广告等核心领域无关，且不包含任何可能迁移到这些领域的技术洞察。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 05:28:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17173v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17173v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    We study a web-deployed, tool-augmented LLM health coach with real users. In a pilot with seven users (280 rated turns), offline policy evaluation (OPE) over factorized decision heads (Tool/Style) shows that a uniform heavy-tool policy raises average value on logs but harms specific subgroups, most notably low-health-literacy/high-self-efficacy users. A lightweight simulator with hidden archetypes further shows that adding a small early information-gain bonus reliably shortens trait identification and improves goal success and pass@3. Together, these early findings indicate an evaluation-first path to personalization: freeze the generator, learn subgroup-aware decision heads on typed rewards (objective tool outcomes and satisfaction), and always report per-archetype metrics to surface subgroup harms that averages obscure.
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            <a href="https://www.alphaxiv.org/abs/2510.17168v1" target="_blank" rel="noopener noreferrer">
                当AI伴侣变得风趣：人脑能否识别AI生成的讽刺？
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            When AI companions become witty: Can human brain recognize AI-generated irony?
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaohui Rao, Hanlin Wu, Zhenguang G. Cai
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究人脑对AI生成讽刺内容的识别能力，属于认知科学和人类-AI交互领域。这与我的核心关注点（推荐系统、搜索、广告中的技术进展）完全无关，不涉及任何推荐算法、Transformer架构改进或LLM在商业应用中的直接使用。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 05:15:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17168v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17168v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As Large Language Models (LLMs) are increasingly deployed as social agents and trained to produce humor and irony, a question emerges: when encountering witty AI remarks, do people interpret these as intentional communication or mere computational output? This study investigates whether people adopt the intentional stance, attributing mental states to explain behavior,toward AI during irony comprehension. Irony provides an ideal paradigm because it requires distinguishing intentional contradictions from unintended errors through effortful semantic reanalysis. We compared behavioral and neural responses to ironic statements from AI versus human sources using established ERP components: P200 reflecting early incongruity detection and P600 indexing cognitive efforts in reinterpreting incongruity as deliberate irony. Results demonstrate that people do not fully adopt the intentional stance toward AI-generated irony. Behaviorally, participants attributed incongruity to deliberate communication for both sources, though significantly less for AI than human, showing greater tendency to interpret AI incongruities as computational errors. Neural data revealed attenuated P200 and P600 effects for AI-generated irony, suggesting reduced effortful detection and reanalysis consistent with diminished attribution of communicative intent. Notably, people who perceived AI as more sincere showed larger P200 and P600 effects for AI-generated irony, suggesting that intentional stance adoption is calibrated by specific mental models of artificial agents. These findings reveal that source attribution shapes neural processing of social-communicative phenomena. Despite current LLMs' linguistic sophistication, achieving genuine social agency requires more than linguistic competence, it necessitates a shift in how humans perceive and attribute intentionality to artificial agents.
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            <a href="https://www.alphaxiv.org/abs/2510.17109v1" target="_blank" rel="noopener noreferrer">
                多智能体系统的验证感知规划
            </a>
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            Verification-Aware Planning for Multi-Agent Systems
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianyang Xu, Dan Zhang, Kushan Mitra, Estevam Hruschka
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文关注多智能体系统的验证和规划，属于多智能体强化学习或控制系统领域，与推荐系统、搜索或广告的核心技术没有直接关联。论文内容不涉及LLM技术、Transformer架构改进，也没有展示在推荐、搜索或广告场景中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 02:54:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17109v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17109v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.MA</span></div>
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                    Large language model (LLM) agents are increasingly deployed to tackle complex tasks, often necessitating collaboration among multiple specialized agents. However, multi-agent collaboration introduces new challenges in planning, coordination, and verification. Execution failures frequently arise not from flawed reasoning alone, but from subtle misalignments in task interpretation, output format, or inter-agent handoffs. To address these challenges, we present VeriMAP, a framework for multi-agent collaboration with verification-aware planning. The VeriMAP planner decomposes tasks, models subtask dependencies, and encodes planner-defined passing criteria as subtask verification functions (VFs) in Python and natural language. We evaluate VeriMAP on diverse datasets, demonstrating that it outperforms both single- and multi-agent baselines while enhancing system robustness and interpretability. Our analysis highlights how verification-aware planning enables reliable coordination and iterative refinement in multi-agent systems, without relying on external labels or annotations.
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            <a href="https://www.alphaxiv.org/abs/2510.17803v1" target="_blank" rel="noopener noreferrer">
                ConsistEdit：高度一致且精确的无训练视觉编辑
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            ConsistEdit: Highly Consistent and Precise Training-free Visual Editing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zixin Yin, Ling-Hao Chen, Lionel Ni, Xili Dai
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视觉编辑技术，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术无关。即使考虑视觉语言模型的类比，该论文关注的是图像编辑的一致性而非多模态统一建模，无法为处理异构数据提供有价值的启示。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:59:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17803v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17803v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in training-free attention control methods have enabled flexible and efficient text-guided editing capabilities for existing generation models. However, current approaches struggle to simultaneously deliver strong editing strength while preserving consistency with the source. This limitation becomes particularly critical in multi-round and video editing, where visual errors can accumulate over time. Moreover, most existing methods enforce global consistency, which limits their ability to modify individual attributes such as texture while preserving others, thereby hindering fine-grained editing. Recently, the architectural shift from U-Net to MM-DiT has brought significant improvements in generative performance and introduced a novel mechanism for integrating text and vision modalities. These advancements pave the way for overcoming challenges that previous methods failed to resolve. Through an in-depth analysis of MM-DiT, we identify three key insights into its attention mechanisms. Building on these, we propose ConsistEdit, a novel attention control method specifically tailored for MM-DiT. ConsistEdit incorporates vision-only attention control, mask-guided pre-attention fusion, and differentiated manipulation of the query, key, and value tokens to produce consistent, prompt-aligned edits. Extensive experiments demonstrate that ConsistEdit achieves state-of-the-art performance across a wide range of image and video editing tasks, including both structure-consistent and structure-inconsistent scenarios. Unlike prior methods, it is the first approach to perform editing across all inference steps and attention layers without handcraft, significantly enhancing reliability and consistency, which enables robust multi-round and multi-region editing. Furthermore, it supports progressive adjustment of structural consistency, enabling finer control.
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            <a href="https://www.alphaxiv.org/abs/2510.17783v1" target="_blank" rel="noopener noreferrer">
                植物学机器人：利用高斯泼溅对遮挡及叶下植物结构进行数字孪生监测
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            Botany-Bot: Digital Twin Monitoring of Occluded and Underleaf Plant Structures with Gaussian Splats
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Simeon Adebola, Chung Min Kim, Justin Kerr, Shuangyu Xie, Prithvi Akella, Jose L...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于植物学领域的数字孪生和计算机视觉技术，涉及植物结构监测和遮挡处理。这与推荐系统、搜索或广告的核心领域完全无关，也不涉及LLM、Transformer架构或异构数据建模等使能技术。该研究属于纯粹的植物学应用领域，属于明确的无关主题。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:42:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17783v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17783v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Commercial plant phenotyping systems using fixed cameras cannot perceive many plant details due to leaf occlusion. In this paper, we present Botany-Bot, a system for building detailed "annotated digital twins" of living plants using two stereo cameras, a digital turntable inside a lightbox, an industrial robot arm, and 3D segmentated Gaussian Splat models. We also present robot algorithms for manipulating leaves to take high-resolution indexable images of occluded details such as stem buds and the underside/topside of leaves. Results from experiments suggest that Botany-Bot can segment leaves with 90.8% accuracy, detect leaves with 86.2% accuracy, lift/push leaves with 77.9% accuracy, and take detailed overside/underside images with 77.3% accuracy. Code, videos, and datasets are available at https://berkeleyautomation.github.io/Botany-Bot/.
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            <a href="https://www.alphaxiv.org/abs/2510.17773v1" target="_blank" rel="noopener noreferrer">
                迈向可解释的皮肤癌分类：一种融合病灶分割与临床元数据的双网络注意力模型
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            Towards Explainable Skin Cancer Classification: A Dual-Network Attention Model with Lesion Segmentation and Clinical Metadata Fusion
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Md. Enamul Atiq, Shaikh Anowarul Fattah
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的皮肤癌分类应用，属于明确的医学领域特定应用。虽然提到了注意力机制和双网络架构，但这些技术应用完全局限于医疗诊断场景，与推荐系统、搜索或广告领域没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 17:33:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17773v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17773v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Skin cancer is a life-threatening disease where early detection significantly improves patient outcomes. Automated diagnosis from dermoscopic images is challenging due to high intra-class variability and subtle inter-class differences. Many deep learning models operate as "black boxes," limiting clinical trust. In this work, we propose a dual-encoder attention-based framework that leverages both segmented lesions and clinical metadata to enhance skin lesion classification in terms of both accuracy and interpretability. A novel Deep-UNet architecture with Dual Attention Gates (DAG) and Atrous Spatial Pyramid Pooling (ASPP) is first employed to segment lesions. The classification stage uses two DenseNet201 encoders-one on the original image and another on the segmented lesion whose features are fused via multi-head cross-attention. This dual-input design guides the model to focus on salient pathological regions. In addition, a transformer-based module incorporates patient metadata (age, sex, lesion site) into the prediction. We evaluate our approach on the HAM10000 dataset and the ISIC 2018 and 2019 challenges. The proposed method achieves state-of-the-art segmentation performance and significantly improves classification accuracy and average AUC compared to baseline models. To validate our model's reliability, we use Gradient-weighted Class Activation Mapping (Grad-CAM) to generate heatmaps. These visualizations confirm that our model's predictions are based on the lesion area, unlike models that rely on spurious background features. These results demonstrate that integrating precise lesion segmentation and clinical data with attention-based fusion leads to a more accurate and interpretable skin cancer classification model.
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            <a href="https://www.alphaxiv.org/abs/2510.17731v1" target="_blank" rel="noopener noreferrer">
                图像到视频模型能否模拟行人动态？
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            Can Image-To-Video Models Simulate Pedestrian Dynamics?
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Aaron Appelle, Jerome P. Lynch
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的行人动态模拟，属于纯粹的视觉应用范畴。虽然涉及视频生成技术，但行人动态模拟与推荐系统、搜索或广告的核心技术需求没有直接关联，也不涉及LLM技术或Transformer架构的进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:44:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17731v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17731v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent high-performing image-to-video (I2V) models based on variants of the diffusion transformer (DiT) have displayed remarkable inherent world-modeling capabilities by virtue of training on large scale video datasets. We investigate whether these models can generate realistic pedestrian movement patterns in crowded public scenes. Our framework conditions I2V models on keyframes extracted from pedestrian trajectory benchmarks, then evaluates their trajectory prediction performance using quantitative measures of pedestrian dynamics.
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                签名伪造检测：提升跨数据集泛化能力
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            Signature Forgery Detection: Improving Cross-Dataset Generalization
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Matheus Ramos Parracho
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于签名伪造检测这一特定计算机视觉任务，属于生物特征安全认证领域。这与推荐系统、搜索或广告的核心技术无关，也不涉及LLM、Transformer架构或异构数据统一建模等关注点。该研究属于安全认证范畴，属于明确排除的无关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:42:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17724v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17724v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Automated signature verification is a critical biometric technique used in banking, identity authentication, and legal documentation. Despite the notable progress achieved by deep learning methods, most approaches in offline signature verification still struggle to generalize across datasets, as variations in handwriting styles and acquisition protocols often degrade performance. This study investigates feature learning strategies for signature forgery detection, focusing on improving cross-dataset generalization -- that is, model robustness when trained on one dataset and tested on another. Using three public benchmarks -- CEDAR, ICDAR, and GPDS Synthetic -- two experimental pipelines were developed: one based on raw signature images and another employing a preprocessing method referred to as shell preprocessing. Several behavioral patterns were identified and analyzed; however, no definitive superiority between the two approaches was established. The results show that the raw-image model achieved higher performance across benchmarks, while the shell-based model demonstrated promising potential for future refinement toward robust, cross-domain signature verification.
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            <a href="https://www.alphaxiv.org/abs/2510.17719v1" target="_blank" rel="noopener noreferrer">
                Raindrop GS：雨滴条件下3D高斯泼溅的基准测试
            </a>
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            Raindrop GS: A Benchmark for 3D Gaussian Splatting under Raindrop Conditions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiqiang Teng, Beibei Lin, Tingting Chen, Zifeng Yuan, Xuanyi Li, Xuanyu Zhang, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D高斯泼溅在雨滴条件下的基准测试，属于纯粹的计算机视觉和3D图形领域。虽然3D高斯泼溅是计算机视觉中的新兴技术，但该论文的具体应用场景（雨滴条件）和基准测试性质与推荐系统、搜索或广告的核心技术栈没有直接关联，也没有展示出在异构数据处理或Transformer架构方面的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:36:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17719v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17719v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D Gaussian Splatting (3DGS) under raindrop conditions suffers from severe occlusions and optical distortions caused by raindrop contamination on the camera lens, substantially degrading reconstruction quality. Existing benchmarks typically evaluate 3DGS using synthetic raindrop images with known camera poses (constrained images), assuming ideal conditions. However, in real-world scenarios, raindrops often interfere with accurate camera pose estimation and point cloud initialization. Moreover, a significant domain gap between synthetic and real raindrops further impairs generalization. To tackle these issues, we introduce RaindropGS, a comprehensive benchmark designed to evaluate the full 3DGS pipeline-from unconstrained, raindrop-corrupted images to clear 3DGS reconstructions. Specifically, the whole benchmark pipeline consists of three parts: data preparation, data processing, and raindrop-aware 3DGS evaluation, including types of raindrop interference, camera pose estimation and point cloud initialization, single image rain removal comparison, and 3D Gaussian training comparison. First, we collect a real-world raindrop reconstruction dataset, in which each scene contains three aligned image sets: raindrop-focused, background-focused, and rain-free ground truth, enabling a comprehensive evaluation of reconstruction quality under different focus conditions. Through comprehensive experiments and analyses, we reveal critical insights into the performance limitations of existing 3DGS methods on unconstrained raindrop images and the varying impact of different pipeline components: the impact of camera focus position on 3DGS reconstruction performance, and the interference caused by inaccurate pose and point cloud initialization on reconstruction. These insights establish clear directions for developing more robust 3DGS methods under raindrop conditions.
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            <a href="https://www.alphaxiv.org/abs/2510.17716v1" target="_blank" rel="noopener noreferrer">
                基于多通道流式细胞成像的循环血细胞簇自动分类
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            Automatic Classification of Circulating Blood Cell Clusters based on Multi-channel Flow Cytometry Imaging
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Suqiang Ma, Subhadeep Sengupta, Yao Lee, Beikang Gu, Xianyan Chen, Xianqiao Wang...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的血细胞分类应用，属于生物医学图像分析范畴。这与搜索、推荐、广告系统或LLM技术没有任何关联，完全超出了您关注的技术领域范围。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:32:23
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17716v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17716v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Circulating blood cell clusters (CCCs) containing red blood cells (RBCs), white blood cells(WBCs), and platelets are significant biomarkers linked to conditions like thrombosis, infection, and inflammation. Flow cytometry, paired with fluorescence staining, is commonly used to analyze these cell clusters, revealing cell morphology and protein profiles. While computational approaches based on machine learning have advanced the automatic analysis of single-cell flow cytometry images, there is a lack of effort to build tools to automatically analyze images containing CCCs. Unlike single cells, cell clusters often exhibit irregular shapes and sizes. In addition, these cell clusters often consist of heterogeneous cell types, which require multi-channel staining to identify the specific cell types within the clusters. This study introduces a new computational framework for analyzing CCC images and identifying cell types within clusters. Our framework uses a two-step analysis strategy. First, it categorizes images into cell cluster and non-cluster groups by fine-tuning the You Only Look Once(YOLOv11) model, which outperforms traditional convolutional neural networks (CNNs), Vision Transformers (ViT). Then, it identifies cell types by overlaying cluster contours with regions from multi-channel fluorescence stains, enhancing accuracy despite cell debris and staining artifacts. This approach achieved over 95% accuracy in both cluster classification and phenotype identification. In summary, our automated framework effectively analyzes CCC images from flow cytometry, leveraging both bright-field and fluorescence data. Initially tested on blood cells, it holds potential for broader applications, such as analyzing immune and tumor cell clusters, supporting cellular research across various diseases.
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            <a href="https://www.alphaxiv.org/abs/2510.17703v1" target="_blank" rel="noopener noreferrer">
                通过基于分块分析手绘图案改进帕金森病检测的跨患者泛化能力
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            Improving Cross-Patient Generalization in Parkinson's Disease Detection through Chunk-Based Analysis of Hand-Drawn Patterns
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mhd Adnan Albani, Riad Sonbol
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于帕金森病的医学检测应用，属于明确的医学领域特定应用，与推荐系统、搜索或广告完全无关。论文内容涉及医疗诊断和疾病检测，属于明确排除的医学应用范畴，没有任何潜在的相关性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:18:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17703v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17703v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Parkinson's disease (PD) is a neurodegenerative disease affecting about 1% of people over the age of 60, causing motor impairments that impede hand coordination activities such as writing and drawing. Many approaches have tried to support early detection of Parkinson's disease based on hand-drawn images; however, we identified two major limitations in the related works: (1) the lack of sufficient datasets, (2) the robustness when dealing with unseen patient data. In this paper, we propose a new approach to detect Parkinson's disease that consists of two stages: The first stage classifies based on their drawing type(circle, meander, spiral), and the second stage extracts the required features from the images and detects Parkinson's disease. We overcame the previous two limitations by applying a chunking strategy where we divide each image into 2x2 chunks. Each chunk is processed separately when extracting features and recognizing Parkinson's disease indicators. To make the final classification, an ensemble method is used to merge the decisions made from each chunk. Our evaluation shows that our proposed approach outperforms the top performing state-of-the-art approaches, in particular on unseen patients. On the NewHandPD dataset our approach, it achieved 97.08% accuracy for seen patients and 94.91% for unseen patients, our proposed approach maintained a gap of only 2.17 percentage points, compared to the 4.76-point drop observed in prior work.
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            <a href="https://www.alphaxiv.org/abs/2510.17686v1" target="_blank" rel="noopener noreferrer">
                迈向开放世界中的3D物体性学习
            </a>
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            Towards 3D Objectness Learning in an Open World
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Taichi Liu, Zhenyu Wang, Ruofeng Liu, Guang Wang, Desheng Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于3D视觉和物体检测，属于纯粹的计算机视觉研究。虽然标题提到'开放世界'概念，但核心是3D物体识别，与推荐系统、搜索或广告的排名和建模需求没有直接关联。该技术缺乏在RecSys/Search/Ads领域的明显应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:01:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17686v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17686v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advancements in 3D object detection and novel category detection have made significant progress, yet research on learning generalized 3D objectness remains insufficient. In this paper, we delve into learning open-world 3D objectness, which focuses on detecting all objects in a 3D scene, including novel objects unseen during training. Traditional closed-set 3D detectors struggle to generalize to open-world scenarios, while directly incorporating 3D open-vocabulary models for open-world ability struggles with vocabulary expansion and semantic overlap. To achieve generalized 3D object discovery, We propose OP3Det, a class-agnostic Open-World Prompt-free 3D Detector to detect any objects within 3D scenes without relying on hand-crafted text prompts. We introduce the strong generalization and zero-shot capabilities of 2D foundation models, utilizing both 2D semantic priors and 3D geometric priors for class-agnostic proposals to broaden 3D object discovery. Then, by integrating complementary information from point cloud and RGB image in the cross-modal mixture of experts, OP3Det dynamically routes uni-modal and multi-modal features to learn generalized 3D objectness. Extensive experiments demonstrate the extraordinary performance of OP3Det, which significantly surpasses existing open-world 3D detectors by up to 16.0% in AR and achieves a 13.5% improvement compared to closed-world 3D detectors.
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                智能通信混合专家增强的医学图像分割基础模型
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            Intelligent Communication Mixture-of-Experts Boosted-Medical Image Segmentation Foundation Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinwei Zhang, Hu Chen, Zhe Yuan, Sukun Tian, Peng Feng
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文明确聚焦于医学图像分割领域，属于医疗应用范畴，这在无关主题中被明确排除。虽然提到了混合专家(MoE)架构，但其应用场景完全限定在医学图像处理，与推荐系统、搜索或广告没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 16:00:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17684v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17684v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Foundation models for medical image segmentation have achieved remarkable performance. Adaptive fine-tuning of natural image segmentation foundation models is crucial for medical image segmentation tasks. However, some limitations exist in existing fine-tuning methods: 1) insufficient representation of high-level features and 2) the fine-tuning process disrupts the structural integrity of pretrained weights. Inspired by these critical problems, we propose an intelligent communication mixture-of-experts boosted-medical image segmentation foundation model, named IC-MoE, with twofold ideas: 1) We construct basic experts, semantic experts, and adaptive experts. Moreover, we implement a pixel probability adaptive voting strategy, which enables expert selection and fusion through label consistency and load balancing. This approach preliminarily enhances the representation capability of high-level features while preserving the structural integrity of pretrained weights. 2) We propose a semantic-guided contrastive learning method to address the issue of weak supervision in contrastive learning. This method further enhances the representation capability of high-level features while preserving the structural integrity of pretrained weights. Extensive experiments across three public medical image segmentation datasets demonstrate that the IC-MoE outperforms other SOTA models. Consequently, the proposed IC-MoE effectively supplements foundational medical image segmentation models with high-level features and pretrained structural integrity. We also validate the superior generalizability of the IC-MoE across diverse medical image segmentation scenarios.
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            <a href="https://www.alphaxiv.org/abs/2510.17681v1" target="_blank" rel="noopener noreferrer">
                PICABench：我们距离物理真实图像编辑还有多远？
            </a>
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        <div class="mb-2 text-base text-gray-700">
            PICABench: How Far Are We from Physically Realistic Image Editing?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuandong Pu, Le Zhuo, Songhao Han, Jinbo Xing, Kaiwen Zhu, Shuo Cao, Bin Fu, Si ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像编辑的物理真实性问题，属于纯粹的计算机视觉领域，与推荐系统、搜索或广告的核心技术无关。虽然图像编辑技术可能间接应用于广告创意生成，但这属于明确排除的无关主题范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:53:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17681v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17681v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Image editing has achieved remarkable progress recently. Modern editing models could already follow complex instructions to manipulate the original content. However, beyond completing the editing instructions, the accompanying physical effects are the key to the generation realism. For example, removing an object should also remove its shadow, reflections, and interactions with nearby objects. Unfortunately, existing models and benchmarks mainly focus on instruction completion but overlook these physical effects. So, at this moment, how far are we from physically realistic image editing? To answer this, we introduce PICABench, which systematically evaluates physical realism across eight sub-dimension (spanning optics, mechanics, and state transitions) for most of the common editing operations (add, remove, attribute change, etc). We further propose the PICAEval, a reliable evaluation protocol that uses VLM-as-a-judge with per-case, region-level human annotations and questions. Beyond benchmarking, we also explore effective solutions by learning physics from videos and construct a training dataset PICA-100K. After evaluating most of the mainstream models, we observe that physical realism remains a challenging problem with large rooms to explore. We hope that our benchmark and proposed solutions can serve as a foundation for future work moving from naive content editing toward physically consistent realism.
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            <a href="https://www.alphaxiv.org/abs/2510.17664v1" target="_blank" rel="noopener noreferrer">
                4DSegStreamer：通过双线程实现流式4D全景分割
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            4DSegStreamer: Streaming 4D Panoptic Segmentation via Dual Threads
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ling Liu, Jun Tian, Li Yi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的4D全景分割技术，属于纯粹的视觉处理领域。虽然标题提到流式处理，但核心内容与推荐系统、搜索或广告的异构数据建模没有直接关联，也不涉及Transformer架构改进或LLM技术应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:37:49
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17664v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17664v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    4D panoptic segmentation in a streaming setting is critical for highly dynamic environments, such as evacuating dense crowds and autonomous driving in complex scenarios, where real-time, fine-grained perception within a constrained time budget is essential. In this paper, we introduce 4DSegStreamer, a novel framework that employs a Dual-Thread System to efficiently process streaming frames. The framework is general and can be seamlessly integrated into existing 3D and 4D segmentation methods to enable real-time capability. It also demonstrates superior robustness compared to existing streaming perception approaches, particularly under high FPS conditions. The system consists of a predictive thread and an inference thread. The predictive thread leverages historical motion and geometric information to extract features and forecast future dynamics. The inference thread ensures timely prediction for incoming frames by aligning with the latest memory and compensating for ego-motion and dynamic object movements. We evaluate 4DSegStreamer on the indoor HOI4D dataset and the outdoor SemanticKITTI and nuScenes datasets. Comprehensive experiments demonstrate the effectiveness of our approach, particularly in accurately predicting dynamic objects in complex scenes.
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            <a href="https://www.alphaxiv.org/abs/2510.17651v1" target="_blank" rel="noopener noreferrer">
                用于暴力检测的节俭联邦学习：LoRA微调视觉语言模型与个性化CNN的比较
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            Frugal Federated Learning for Violence Detection: A Comparison of LoRA-Tuned VLMs and Personalized CNNs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sébastien Thuau, Siba Haidar, Ayush Bajracharya, Rachid Chelouah
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注暴力检测这一计算机视觉应用，属于明确的无关主题。虽然涉及联邦学习（被列为无关主题）和LoRA微调技术，但核心应用领域与推荐系统、搜索或广告完全无关，且没有证据表明这些技术会被应用于相关领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:26:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17651v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17651v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    We examine frugal federated learning approaches to violence detection by comparing two complementary strategies: (i) zero-shot and federated fine-tuning of vision-language models (VLMs), and (ii) personalized training of a compact 3D convolutional neural network (CNN3D). Using LLaVA-7B and a 65.8M parameter CNN3D as representative cases, we evaluate accuracy, calibration, and energy usage under realistic non-IID settings. Both approaches exceed 90% accuracy. CNN3D slightly outperforms Low-Rank Adaptation(LoRA)-tuned VLMs in ROC AUC and log loss, while using less energy. VLMs remain favorable for contextual reasoning and multimodal inference. We quantify energy and CO$_2$ emissions across training and inference, and analyze sustainability trade-offs for deployment. To our knowledge, this is the first comparative study of LoRA-tuned vision-language models and personalized CNNs for federated violence detection, with an emphasis on energy efficiency and environmental metrics. These findings support a hybrid model: lightweight CNNs for routine classification, with selective VLM activation for complex or descriptive scenarios. The resulting framework offers a reproducible baseline for responsible, resource-aware AI in video surveillance, with extensions toward real-time, multimodal, and lifecycle-aware systems.
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            <a href="https://www.alphaxiv.org/abs/2510.17650v1" target="_blank" rel="noopener noreferrer">
                ZACH-ViT：一种用于稳健肺部超声分类的零令牌视觉Transformer与ShuffleStrides数据增强方法
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            ZACH-ViT: A Zero-Token Vision Transformer with ShuffleStrides Data Augmentation for Robust Lung Ultrasound Classification
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Athanasios Angelakis, Amne Mousa, Micah L. A. Heldeweg, Laurens A. Biesheuvel, M...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像（肺部超声）分类，属于明确的医学领域应用。虽然涉及Transformer架构，但其应用场景与推荐系统、搜索或广告完全无关，且没有展示任何在这些领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:26:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17650v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17650v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Differentiating cardiogenic pulmonary oedema (CPE) from non-cardiogenic and structurally normal lungs in lung ultrasound (LUS) videos remains challenging due to the high visual variability of non-cardiogenic inflammatory patterns (NCIP/ARDS-like), interstitial lung disease, and healthy lungs. This heterogeneity complicates automated classification as overlapping B-lines and pleural artefacts are common. We introduce ZACH-ViT (Zero-token Adaptive Compact Hierarchical Vision Transformer), a 0.25 M-parameter Vision Transformer variant that removes both positional embeddings and the [CLS] token, making it fully permutation-invariant and suitable for unordered medical image data. To enhance generalization, we propose ShuffleStrides Data Augmentation (SSDA), which permutes probe-view sequences and frame orders while preserving anatomical validity. ZACH-ViT was evaluated on 380 LUS videos from 95 critically ill patients against nine state-of-the-art baselines. Despite the heterogeneity of the non-cardiogenic group, ZACH-ViT achieved the highest validation and test ROC-AUC (0.80 and 0.79) with balanced sensitivity (0.60) and specificity (0.91), while all competing models collapsed to trivial classification. It trains 1.35x faster than Minimal ViT (0.62M parameters) with 2.5x fewer parameters, supporting real-time clinical deployment. These results show that aligning architectural design with data structure can outperform scale in small-data medical imaging.
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            <a href="https://www.alphaxiv.org/abs/2510.17644v1" target="_blank" rel="noopener noreferrer">
                基于高分辨率数字高程模型导数的历史景观考古石墙测绘自监督预训练
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            Self-supervised Pre-training for Mapping of Archaeological Stone Wall in Historic Landscapes Using High-Resolution DEM Derivatives
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zexian Huang, Mashnoon Islam, Brian Armstrong, Kourosh Khoshelham, Martin Tomko
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于考古学和遥感领域的特定应用，涉及历史景观中的石墙测绘。虽然使用了自监督预训练技术，但其应用领域（考古学）和数据类型（DEM导数）与推荐系统、搜索或广告的核心技术栈完全无关。该研究没有展示任何在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:23:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17644v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17644v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Dry-stone walls hold significant heritage and environmental value. Mapping these structures is essential for ecosystem preservation and wildfire management in Australia. Yet, many walls remain unidentified due to their inaccessibility and the high cost of manual mapping. Deep learning-based segmentation offers a scalable solution, but two major challenges persist: (1) visual occlusion of low-lying walls by dense vegetation, and (2) limited labeled data for supervised training. We propose DINO-CV, a segmentation framework for automatic mapping of low-lying dry-stone walls using high-resolution Airborne LiDAR-derived digital elevation models (DEMs). DEMs overcome visual occlusion by capturing terrain structures hidden beneath vegetation, enabling analysis of structural rather than spectral cues. DINO-CV introduces a self-supervised cross-view pre-training strategy based on knowledge distillation to mitigate data scarcity. It learns invariant visual and geometric representations across multiple DEM derivatives, supporting various vision backbones including ResNet, Wide ResNet, and Vision Transformers. Applied to the UNESCO World Heritage cultural landscape of Budj Bim, Victoria, the method identifies one of Australia's densest collections of colonial dry-stone walls beyond Indigenous heritage contexts. DINO-CV achieves a mean Intersection over Union (mIoU) of 68.6% on test areas and maintains 63.8% mIoU when fine-tuned with only 10% labeled data. These results demonstrate the potential of self-supervised learning on high-resolution DEM derivatives for automated dry-stone wall mapping in vegetated and heritage-rich environments with scarce annotations.
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            <a href="https://www.alphaxiv.org/abs/2510.17626v1" target="_blank" rel="noopener noreferrer">
                CaMiT：用于分类和生成的时序感知汽车模型数据集
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            CaMiT: A Time-Aware Car Model Dataset for Classification and Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Frédéric LIN, Biruk Abere Ambaw, Adrian Popescu, Hejer Ammar, Romaric Audigier, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注汽车模型数据集，属于特定领域的数据集构建工作，与推荐系统、搜索或广告的核心技术进展无关。虽然涉及分类任务，但汽车模型这一特定领域应用与当前关注的LLM技术、Transformer架构进展或异构数据统一建模没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:11:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17626v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17626v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    AI systems must adapt to evolving visual environments, especially in domains where object appearances change over time. We introduce Car Models in Time (CaMiT), a fine-grained dataset capturing the temporal evolution of car models, a representative class of technological artifacts. CaMiT includes 787K labeled samples of 190 car models (2007-2023) and 5.1M unlabeled samples (2005-2023), supporting both supervised and self-supervised learning. Static pretraining on in-domain data achieves competitive performance with large-scale generalist models while being more resource-efficient, yet accuracy declines when models are tested across years. To address this, we propose a time-incremental classification setting, a realistic continual learning scenario with emerging, evolving, and disappearing classes. We evaluate two strategies: time-incremental pretraining, which updates the backbone, and time-incremental classifier learning, which updates only the final layer, both improving temporal robustness. Finally, we explore time-aware image generation that leverages temporal metadata during training, yielding more realistic outputs. CaMiT offers a rich benchmark for studying temporal adaptation in fine-grained visual recognition and generation.
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            <a href="https://www.alphaxiv.org/abs/2510.17617v1" target="_blank" rel="noopener noreferrer">
                ImaGGen：基于语言和图像输入生成语义协调的零样本语音手势
            </a>
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            ImaGGen: Zero-Shot Generation of Co-Speech Semantic Gestures Grounded in Language and Image Input
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hendric Voss, Stefan Kopp
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于语音手势生成，属于多模态生成任务，与推荐系统、搜索或广告的核心技术领域无关。虽然涉及语言和图像输入，但其应用场景（语音手势）在RecSys/Search/Ads领域没有明确的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 15:01:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17617v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17617v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.HC</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Human communication combines speech with expressive nonverbal cues such as hand gestures that serve manifold communicative functions. Yet, current generative gesture generation approaches are restricted to simple, repetitive beat gestures that accompany the rhythm of speaking but do not contribute to communicating semantic meaning. This paper tackles a core challenge in co-speech gesture synthesis: generating iconic or deictic gestures that are semantically coherent with a verbal utterance. Such gestures cannot be derived from language input alone, which inherently lacks the visual meaning that is often carried autonomously by gestures. We therefore introduce a zero-shot system that generates gestures from a given language input and additionally is informed by imagistic input, without manual annotation or human intervention. Our method integrates an image analysis pipeline that extracts key object properties such as shape, symmetry, and alignment, together with a semantic matching module that links these visual details to spoken text. An inverse kinematics engine then synthesizes iconic and deictic gestures and combines them with co-generated natural beat gestures for coherent multimodal communication. A comprehensive user study demonstrates the effectiveness of our approach. In scenarios where speech alone was ambiguous, gestures generated by our system significantly improved participants' ability to identify object properties, confirming their interpretability and communicative value. While challenges remain in representing complex shapes, our results highlight the importance of context-aware semantic gestures for creating expressive and collaborative virtual agents or avatars, marking a substantial step forward towards efficient and robust, embodied human-agent interaction. More information and example videos are available here: https://review-anon-io.github.io/ImaGGen.github.io/
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            <a href="https://www.alphaxiv.org/abs/2510.17609v1" target="_blank" rel="noopener noreferrer">
                集成BIM与无人机摄影测量技术实现自动化三维结构模型分割
            </a>
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            Integrating BIM and UAV-based photogrammetry for Automated 3D Structure Model Segmentation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Siqi Chen, Shanyue Guan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于建筑信息模型（BIM）和无人机摄影测量的集成应用，属于建筑和土木工程领域的特定技术应用。与推荐系统、搜索、广告或LLM技术没有任何直接或间接的关联，也不涉及Transformer架构或异构数据处理。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:54:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17609v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17609v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The advancement of UAV technology has enabled efficient, non-contact structural health monitoring. Combined with photogrammetry, UAVs can capture high-resolution scans and reconstruct detailed 3D models of infrastructure. However, a key challenge remains in segmenting specific structural components from these models-a process traditionally reliant on time-consuming and error-prone manual labeling. To address this issue, we propose a machine learning-based framework for automated segmentation of 3D point clouds. Our approach uses the complementary strengths of real-world UAV-scanned point clouds and synthetic data generated from Building Information Modeling (BIM) to overcome the limitations associated with manual labeling. Validation on a railroad track dataset demonstrated high accuracy in identifying and segmenting major components such as rails and crossties. Moreover, by using smaller-scale datasets supplemented with BIM data, the framework significantly reduced training time while maintaining reasonable segmentation accuracy. This automated approach improves the precision and efficiency of 3D infrastructure model segmentation and advances the integration of UAV and BIM technologies in structural health monitoring and infrastructure management.
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            <a href="https://www.alphaxiv.org/abs/2510.17599v1" target="_blank" rel="noopener noreferrer">
                通过手势传达意义：语义协同语音手势生成的调查研究
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            Conveying Meaning through Gestures: An Investigation into Semantic Co-Speech Gesture Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hendric Voss, Lisa Michelle Bohnenkamp, Stefan Kopp
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于手势生成和语音交互，属于多模态人机交互领域。该主题与搜索、推荐或广告系统没有直接关联，也不涉及Transformer架构改进或LLM技术。手势生成技术在当前阶段对RecSys/Search/Ads领域没有明显的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:47:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17599v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17599v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.HC</span><span class="category-tag">cs.CV</span></div>
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                    This study explores two frameworks for co-speech gesture generation, AQ-GT and its semantically-augmented variant AQ-GT-a, to evaluate their ability to convey meaning through gestures and how humans perceive the resulting movements. Using sentences from the SAGA spatial communication corpus, contextually similar sentences, and novel movement-focused sentences, we conducted a user-centered evaluation of concept recognition and human-likeness. Results revealed a nuanced relationship between semantic annotations and performance. The original AQ-GT framework, lacking explicit semantic input, was surprisingly more effective at conveying concepts within its training domain. Conversely, the AQ-GT-a framework demonstrated better generalization, particularly for representing shape and size in novel contexts. While participants rated gestures from AQ-GT-a as more expressive and helpful, they did not perceive them as more human-like. These findings suggest that explicit semantic enrichment does not guarantee improved gesture generation and that its effectiveness is highly dependent on the context, indicating a potential trade-off between specialization and generalization.
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            <a href="https://www.alphaxiv.org/abs/2510.17585v1" target="_blank" rel="noopener noreferrer">
                揭露水中伪装：水下伪装实例分割与数据集
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            Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chuhong Wang, Hua Li, Chongyi Li, Huazhong Liu, Xiongxin Tang, Sam Kwong
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于水下计算机视觉中的伪装实例分割，属于纯粹的视觉领域研究。论文内容涉及水下环境中的目标检测和分割，与推荐系统、搜索或广告的核心技术领域没有任何关联，也没有任何潜在的跨领域应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:34:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17585v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17585v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    With the development of underwater exploration and marine protection, underwater vision tasks are widespread. Due to the degraded underwater environment, characterized by color distortion, low contrast, and blurring, camouflaged instance segmentation (CIS) faces greater challenges in accurately segmenting objects that blend closely with their surroundings. Traditional camouflaged instance segmentation methods, trained on terrestrial-dominated datasets with limited underwater samples, may exhibit inadequate performance in underwater scenes. To address these issues, we introduce the first underwater camouflaged instance segmentation (UCIS) dataset, abbreviated as UCIS4K, which comprises 3,953 images of camouflaged marine organisms with instance-level annotations. In addition, we propose an Underwater Camouflaged Instance Segmentation network based on Segment Anything Model (UCIS-SAM). Our UCIS-SAM includes three key modules. First, the Channel Balance Optimization Module (CBOM) enhances channel characteristics to improve underwater feature learning, effectively addressing the model's limited understanding of underwater environments. Second, the Frequency Domain True Integration Module (FDTIM) is proposed to emphasize intrinsic object features and reduce interference from camouflage patterns, enhancing the segmentation performance of camouflaged objects blending with their surroundings. Finally, the Multi-scale Feature Frequency Aggregation Module (MFFAM) is designed to strengthen the boundaries of low-contrast camouflaged instances across multiple frequency bands, improving the model's ability to achieve more precise segmentation of camouflaged objects. Extensive experiments on the proposed UCIS4K and public benchmarks show that our UCIS-SAM outperforms state-of-the-art approaches.
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            <a href="https://www.alphaxiv.org/abs/2510.17568v1" target="_blank" rel="noopener noreferrer">
                PAGE-4D：面向4D感知的解耦姿态与几何估计
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            PAGE-4D: Disentangled Pose and Geometry Estimation for 4D Perception
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kaichen Zhou, Yuhan Wang, Grace Chen, Xinhai Chang, Gaspard Beaudouin, Fangneng ...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于4D感知中的姿态和几何估计，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然涉及3D/4D视觉技术，但论文标题未表明任何在推荐、搜索或广告领域的潜在应用，完全超出了当前关注的技术范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:17:16
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                <a href="https://arxiv.org/abs/2510.17568v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17568v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent 3D feed-forward models, such as the Visual Geometry Grounded Transformer (VGGT), have shown strong capability in inferring 3D attributes of static scenes. However, since they are typically trained on static datasets, these models often struggle in real-world scenarios involving complex dynamic elements, such as moving humans or deformable objects like umbrellas. To address this limitation, we introduce PAGE-4D, a feedforward model that extends VGGT to dynamic scenes, enabling camera pose estimation, depth prediction, and point cloud reconstruction -- all without post-processing. A central challenge in multi-task 4D reconstruction is the inherent conflict between tasks: accurate camera pose estimation requires suppressing dynamic regions, while geometry reconstruction requires modeling them. To resolve this tension, we propose a dynamics-aware aggregator that disentangles static and dynamic information by predicting a dynamics-aware mask -- suppressing motion cues for pose estimation while amplifying them for geometry reconstruction. Extensive experiments show that PAGE-4D consistently outperforms the original VGGT in dynamic scenarios, achieving superior results in camera pose estimation, monocular and video depth estimation, and dense point map reconstruction.
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            <a href="https://www.alphaxiv.org/abs/2510.17566v1" target="_blank" rel="noopener noreferrer">
                WP-CrackNet：一种用于端到端弱监督道路裂缝检测的协作对抗学习框架
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            WP-CrackNet: A Collaborative Adversarial Learning Framework for End-to-End Weakly-Supervised Road Crack Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nachuan Ma, Zhengfei Song, Qiang Hu, Xiaoyu Tang, Chengxi Zhang, Rui Fan, Lihua ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的道路裂缝检测，属于纯粹的视觉应用，与推荐系统、搜索或广告的核心技术领域无关。论文标题中提到的弱监督学习和对抗学习框架没有显示出在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 14:13:26
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                <a href="https://arxiv.org/abs/2510.17566v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17566v1
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Road crack detection is essential for intelligent infrastructure maintenance in smart cities. To reduce reliance on costly pixel-level annotations, we propose WP-CrackNet, an end-to-end weakly-supervised method that trains with only image-level labels for pixel-wise crack detection. WP-CrackNet integrates three components: a classifier generating class activation maps (CAMs), a reconstructor measuring feature inferability, and a detector producing pixel-wise road crack detection results. During training, the classifier and reconstructor alternate in adversarial learning to encourage crack CAMs to cover complete crack regions, while the detector learns from pseudo labels derived from post-processed crack CAMs. This mutual feedback among the three components improves learning stability and detection accuracy. To further boost detection performance, we design a path-aware attention module (PAAM) that fuses high-level semantics from the classifier with low-level structural cues from the reconstructor by modeling spatial and channel-wise dependencies. Additionally, a center-enhanced CAM consistency module (CECCM) is proposed to refine crack CAMs using center Gaussian weighting and consistency constraints, enabling better pseudo-label generation. We create three image-level datasets and extensive experiments show that WP-CrackNet achieves comparable results to supervised methods and outperforms existing weakly-supervised methods, significantly advancing scalable road inspection. The source code package and datasets are available at https://mias.group/WP-CrackNet/.
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            <a href="https://www.alphaxiv.org/abs/2510.17540v1" target="_blank" rel="noopener noreferrer">
                使用深度学习检测智能望远镜图像中的条纹
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            Detecting streaks in smart telescopes images with Deep Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Olivier Parisot, Mahmoud Jaziri
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于天文学领域的计算机视觉应用，使用深度学习处理望远镜图像中的条纹检测。这与推荐系统、搜索或广告的核心领域完全无关，也不涉及任何可能应用于这些领域的LLM技术或Transformer架构进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 13:43:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17540v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17540v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">astro-ph.IM</span><span class="category-tag">cs.CV</span></div>
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                    The growing negative impact of the visibility of satellites in the night sky is influencing the practice of astronomy and astrophotograph, both at the amateur and professional levels. The presence of these satellites has the effect of introducing streaks into the images captured during astronomical observation, requiring the application of additional post processing to mitigate the undesirable impact, whether for data loss or cosmetic reasons. In this paper, we show how we test and adapt various Deep Learning approaches to detect streaks in raw astronomical data captured between March 2022 and February 2023 with smart telescopes.
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            <a href="https://www.alphaxiv.org/abs/2510.17529v1" target="_blank" rel="noopener noreferrer">
                MambaX-Net：用于纵向MRI分割的双输入Mamba增强交叉注意力网络
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            MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yovin Yahathugoda, Davide Prezzi, Piyalitt Ittichaiwong, Vicky Goh, Sebastien Ou...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像（MRI）分割，属于医疗领域特定应用，与推荐系统、搜索或广告完全无关。虽然提到了Mamba架构，但应用场景仅限于医学图像处理，没有任何潜在的应用于RecSys/Search/Ads的可能性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 13:32:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17529v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17529v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    Active Surveillance (AS) is a treatment option for managing low and intermediate-risk prostate cancer (PCa), aiming to avoid overtreatment while monitoring disease progression through serial MRI and clinical follow-up. Accurate prostate segmentation is an important preliminary step for automating this process, enabling automated detection and diagnosis of PCa. However, existing deep-learning segmentation models are often trained on single-time-point and expertly annotated datasets, making them unsuitable for longitudinal AS analysis, where multiple time points and a scarcity of expert labels hinder their effective fine-tuning. To address these challenges, we propose MambaX-Net, a novel semi-supervised, dual-scan 3D segmentation architecture that computes the segmentation for time point t by leveraging the MRI and the corresponding segmentation mask from the previous time point. We introduce two new components: (i) a Mamba-enhanced Cross-Attention Module, which integrates the Mamba block into cross attention to efficiently capture temporal evolution and long-range spatial dependencies, and (ii) a Shape Extractor Module that encodes the previous segmentation mask into a latent anatomical representation for refined zone delination. Moreover, we introduce a semi-supervised self-training strategy that leverages pseudo-labels generated from a pre-trained nnU-Net, enabling effective learning without expert annotations. MambaX-Net was evaluated on a longitudinal AS dataset, and results showed that it significantly outperforms state-of-the-art U-Net and Transformer-based models, achieving superior prostate zone segmentation even when trained on limited and noisy data.
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            <a href="https://www.alphaxiv.org/abs/2510.17479v1" target="_blank" rel="noopener noreferrer">
                初始化以泛化：面向稀疏视图3D高斯泼溅的更强初始化流程
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            Initialize to Generalize: A Stronger Initialization Pipeline for Sparse-View 3DGS
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Feng Zhou, Wenkai Guo, Pu Cao, Zhicheng Zhang, Jianqin Yin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D高斯泼溅（3DGS）在稀疏视图下的初始化方法，属于计算机视觉和3D重建领域。虽然3DGS是新兴技术，但论文内容主要针对3D视觉的特定技术挑战，没有明确展示在推荐系统、搜索或广告领域的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 12:23:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17479v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17479v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Sparse-view 3D Gaussian Splatting (3DGS) often overfits to the training views, leading to artifacts like blurring in novel view rendering. Prior work addresses it either by enhancing the initialization (\emph{i.e.}, the point cloud from Structure-from-Motion (SfM)) or by adding training-time constraints (regularization) to the 3DGS optimization. Yet our controlled ablations reveal that initialization is the decisive factor: it determines the attainable performance band in sparse-view 3DGS, while training-time constraints yield only modest within-band improvements at extra cost. Given initialization's primacy, we focus our design there. Although SfM performs poorly under sparse views due to its reliance on feature matching, it still provides reliable seed points. Thus, building on SfM, our effort aims to supplement the regions it fails to cover as comprehensively as possible. Specifically, we design: (i) frequency-aware SfM that improves low-texture coverage via low-frequency view augmentation and relaxed multi-view correspondences; (ii) 3DGS self-initialization that lifts photometric supervision into additional points, compensating SfM-sparse regions with learned Gaussian centers; and (iii) point-cloud regularization that enforces multi-view consistency and uniform spatial coverage through simple geometric/visibility priors, yielding a clean and reliable point cloud. Our experiments on LLFF and Mip-NeRF360 demonstrate consistent gains in sparse-view settings, establishing our approach as a stronger initialization strategy. Code is available at https://github.com/zss171999645/ItG-GS.
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            <a href="https://www.alphaxiv.org/abs/2510.17440v1" target="_blank" rel="noopener noreferrer">
                通过可学习色彩空间变换重新思考夜间图像去雨
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            Rethinking Nighttime Image Deraining via Learnable Color Space Transformation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qiyuan Guan, Xiang Chen, Guiyue Jin, Jiyu Jin, Shumin Fan, Tianyu Song, Jinshan ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的夜间图像去雨问题，属于纯粹的图像处理领域。虽然标题提到'可学习变换'，但这与推荐系统、搜索或广告的核心技术没有直接关联。该技术没有明显的应用场景可以转化到异构数据处理或多模态建模中。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 11:28:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17440v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17440v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Compared to daytime image deraining, nighttime image deraining poses significant challenges due to inherent complexities of nighttime scenarios and the lack of high-quality datasets that accurately represent the coupling effect between rain and illumination. In this paper, we rethink the task of nighttime image deraining and contribute a new high-quality benchmark, HQ-NightRain, which offers higher harmony and realism compared to existing datasets. In addition, we develop an effective Color Space Transformation Network (CST-Net) for better removing complex rain from nighttime scenes. Specifically, we propose a learnable color space converter (CSC) to better facilitate rain removal in the Y channel, as nighttime rain is more pronounced in the Y channel compared to the RGB color space. To capture illumination information for guiding nighttime deraining, implicit illumination guidance is introduced enabling the learned features to improve the model's robustness in complex scenarios. Extensive experiments show the value of our dataset and the effectiveness of our method. The source code and datasets are available at https://github.com/guanqiyuan/CST-Net.
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            <a href="https://www.alphaxiv.org/abs/2510.17434v1" target="_blank" rel="noopener noreferrer">
                利用AV1运动矢量实现快速密集特征匹配
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            Leveraging AV1 motion vectors for Fast and Dense Feature Matching
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Julien Zouein, Hossein Javidnia, François Pitié, Anil Kokaram
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频编码中的运动矢量技术，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。虽然特征匹配在广义计算机视觉中有应用，但论文聚焦于AV1编码标准的具体实现，缺乏向推荐/搜索/广告领域的可转移性论证。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 11:22:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17434v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17434v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    We repurpose AV1 motion vectors to produce dense sub-pixel correspondences and short tracks filtered by cosine consistency. On short videos, this compressed-domain front end runs comparably to sequential SIFT while using far less CPU, and yields denser matches with competitive pairwise geometry. As a small SfM demo on a 117-frame clip, MV matches register all images and reconstruct 0.46-0.62M points at 0.51-0.53,px reprojection error; BA time grows with match density. These results show compressed-domain correspondences are a practical, resource-efficient front end with clear paths to scaling in full pipelines.
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            <a href="https://www.alphaxiv.org/abs/2510.17409v1" target="_blank" rel="noopener noreferrer">
                马厩马匹监控：从目标检测到事件检测
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            Monitoring Horses in Stalls: From Object to Event Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dmitrii Galimzianov, Viacheslav Vyshegorodtsev, Ivan Nezhivykh
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉在动物监控领域的应用，涉及目标检测和事件检测技术。虽然这些技术在其他领域可能有价值，但该论文的特定应用场景（马匹监控）与推荐系统、搜索或广告领域没有任何直接或间接关联，也不涉及LLM、Transformer架构或异构数据处理等核心技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:52:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17409v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17409v1
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Monitoring the behavior of stalled horses is essential for early detection of health and welfare issues but remains labor-intensive and time-consuming. In this study, we present a prototype vision-based monitoring system that automates the detection and tracking of horses and people inside stables using object detection and multi-object tracking techniques. The system leverages YOLOv11 and BoT-SORT for detection and tracking, while event states are inferred based on object trajectories and spatial relations within the stall. To support development, we constructed a custom dataset annotated with assistance from foundation models CLIP and GroundingDINO. The system distinguishes between five event types and accounts for the camera's blind spots. Qualitative evaluation demonstrated reliable performance for horse-related events, while highlighting limitations in detecting people due to data scarcity. This work provides a foundation for real-time behavioral monitoring in equine facilities, with implications for animal welfare and stable management.
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            <a href="https://www.alphaxiv.org/abs/2510.17373v1" target="_blank" rel="noopener noreferrer">
                基于特征融合和自适应类别平衡的面部表情帕金森病严重程度诊断
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            Facial Expression-based Parkinson's Disease Severity Diagnosis via Feature Fusion and Adaptive Class Balancing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yintao Zhou, Wei Huang, Zhengyu Li, Jing Huang, Meng Pang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的帕金森病诊断，使用面部表情分析这一计算机视觉技术。这与推荐系统、搜索或广告的核心领域无关，也不涉及LLM技术、Transformer架构进展或异构数据统一建模。该研究属于明确的医疗应用范畴，属于需要排除的领域特定应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 10:09:12
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                <a href="https://arxiv.org/abs/2510.17373v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17373v1
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                    Parkinson's disease (PD) severity diagnosis is crucial for early detecting potential patients and adopting tailored interventions. Diagnosing PD based on facial expression is grounded in PD patients' "masked face" symptom and gains growing interest recently for its convenience and affordability. However, current facial expression-based approaches often rely on single type of expression which can lead to misdiagnosis, and ignore the class imbalance across different PD stages which degrades the prediction performance. Moreover, most existing methods focus on binary classification (i.e., PD / non-PD) rather than diagnosing the severity of PD. To address these issues, we propose a new facial expression-based method for PD severity diagnosis which integrates multiple facial expression features through attention-based feature fusion. Moreover, we mitigate the class imbalance problem via an adaptive class balancing strategy which dynamically adjusts the contribution of training samples based on their class distribution and classification difficulty. Experimental results demonstrate the promising performance of the proposed method for PD severity diagnosis, as well as the efficacy of attention-based feature fusion and adaptive class balancing.
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            <a href="https://www.alphaxiv.org/abs/2510.17338v1" target="_blank" rel="noopener noreferrer">
                基于最近类均值和逻辑值一致性的野生动物开放集识别
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            Nearest-Class Mean and Logits Agreement for Wildlife Open-Set Recognition
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiahao Huo, Mufhumudzi Muthivhi, Terence L. van Zyl, Fredrik Gustafsson
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于野生动物图像识别的开放集问题，属于计算机视觉的特定领域应用。虽然提到了分类和识别技术，但这是纯粹的视觉应用，与推荐系统、搜索或广告没有明确的关联。论文内容主要针对生物学/生态学领域的视觉识别问题，属于明确的无关主题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 09:32:08
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                <a href="https://arxiv.org/abs/2510.17338v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17338v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Current state-of-the-art Wildlife classification models are trained under the closed world setting. When exposed to unknown classes, they remain overconfident in their predictions. Open-set Recognition (OSR) aims to classify known classes while rejecting unknown samples. Several OSR methods have been proposed to model the closed-set distribution by observing the feature, logit, or softmax probability space. A significant drawback of many existing approaches is the requirement to retrain the pre-trained classification model with the OSR-specific strategy. This study contributes a post-processing OSR method that measures the agreement between the models' features and predicted logits. We propose a probability distribution based on an input's distance to its Nearest Class Mean (NCM). The NCM-based distribution is then compared with the softmax probabilities from the logit space to measure agreement between the NCM and the classification head. Our proposed strategy ranks within the top three on two evaluated datasets, showing consistent performance across the two datasets. In contrast, current state-of-the-art methods excel on a single dataset. We achieve an AUROC of 93.41 and 95.35 for African and Swedish animals. The code can be found https://github.com/Applied-Representation-Learning-Lab/OSR.
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            <a href="https://www.alphaxiv.org/abs/2510.17322v1" target="_blank" rel="noopener noreferrer">
                单组对抗性衣物在物理世界中破坏多种防御方法
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            A Single Set of Adversarial Clothes Breaks Multiple Defense Methods in the Physical World
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wei Zhang, Zhanhao Hu, Xiao Li, Xiaopei Zhu, Xiaolin Hu
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及对抗性攻击和物理世界安全防御，属于安全领域，与我的核心关注点（推荐系统、搜索、广告、LLM技术及其应用）完全不相关。文中提到的防御方法破坏属于安全/隐私范畴，属于明确排除的无关主题。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 09:16:25
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                <a href="https://arxiv.org/abs/2510.17322v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17322v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    In recent years, adversarial attacks against deep learning-based object detectors in the physical world have attracted much attention. To defend against these attacks, researchers have proposed various defense methods against adversarial patches, a typical form of physically-realizable attack. However, our experiments showed that simply enlarging the patch size could make these defense methods fail. Motivated by this, we evaluated various defense methods against adversarial clothes which have large coverage over the human body. Adversarial clothes provide a good test case for adversarial defense against patch-based attacks because they not only have large sizes but also look more natural than a large patch on humans. Experiments show that all the defense methods had poor performance against adversarial clothes in both the digital world and the physical world. In addition, we crafted a single set of clothes that broke multiple defense methods on Faster R-CNN. The set achieved an Attack Success Rate (ASR) of 96.06% against the undefended detector and over 64.84% ASRs against nine defended models in the physical world, unveiling the common vulnerability of existing adversarial defense methods against adversarial clothes. Code is available at: https://github.com/weiz0823/adv-clothes-break-multiple-defenses.
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            <a href="https://www.alphaxiv.org/abs/2510.17287v1" target="_blank" rel="noopener noreferrer">
                基于机器视觉的手术照明系统：设计与实现
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            Machine Vision-Based Surgical Lighting System:Design and Implementation
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Amir Gharghabi, Mahdi Hakiminezhad, Maryam Shafaei, Shaghayegh Gharghabi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于机器视觉在医疗手术照明系统中的应用，属于医疗领域的特定应用。标题中没有任何与推荐系统、搜索、广告、LLM技术或Transformer架构相关的关键词，完全超出了我的关注范围。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 08:22:45
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                <a href="https://arxiv.org/abs/2510.17287v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17287v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.HC</span></div>
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                    Effortless and ergonomically designed surgical lighting is critical for precision and safety during procedures. However, traditional systems often rely on manual adjustments, leading to surgeon fatigue, neck strain, and inconsistent illumination due to drift and shadowing. To address these challenges, we propose a novel surgical lighting system that leverages the YOLOv11 object detection algorithm to identify a blue marker placed above the target surgical site. A high-power LED light source is then directed to the identified location using two servomotors equipped with tilt-pan brackets. The YOLO model achieves 96.7% mAP@50 on the validation set consisting of annotated images simulating surgical scenes with the blue spherical marker. By automating the lighting process, this machine vision-based solution reduces physical strain on surgeons, improves consistency in illumination, and supports improved surgical outcomes.
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            <a href="https://www.alphaxiv.org/abs/2510.17278v1" target="_blank" rel="noopener noreferrer">
                SG-CLDFF：一种用于自动化白细胞分类与分割的新型框架
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            SG-CLDFF: A Novel Framework for Automated White Blood Cell Classification and Segmentation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mehdi Zekriyapanah Gashti, Mostafa Mohammadpour, Ghasem Farjamnia
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学图像处理中的白细胞分类和分割，属于生物医学领域的特定应用。这与我的关注点（推荐系统、搜索、广告及其相关技术）完全无关，没有涉及任何推荐算法、Transformer架构改进或LLM技术在商业系统中的应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 08:07:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17278v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17278v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">68T07</span><span class="category-tag">92C55</span><span class="category-tag">I.4.6; I.2.6</span></div>
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                    Accurate segmentation and classification of white blood cells (WBCs) in microscopic images are essential for diagnosis and monitoring of many hematological disorders, yet remain challenging due to staining variability, complex backgrounds, and class imbalance. In this paper, we introduce a novel Saliency-Guided Cross-Layer Deep Feature Fusion framework (SG-CLDFF) that tightly integrates saliency-driven preprocessing with multi-scale deep feature aggregation to improve both robustness and interpretability for WBC analysis. SG-CLDFF first computes saliency priors to highlight candidate WBC regions and guide subsequent feature extraction. A lightweight hybrid backbone (EfficientSwin-style) produces multi-resolution representations, which are fused by a ResNeXt-CC-inspired cross-layer fusion module to preserve complementary information from shallow and deep layers. The network is trained in a multi-task setup with concurrent segmentation and cell-type classification heads, using class-aware weighted losses and saliency-alignment regularization to mitigate imbalance and suppress background activation. Interpretability is enforced through Grad-CAM visualizations and saliency consistency checks, allowing model decisions to be inspected at the regional level. We validate the framework on standard public benchmarks (BCCD, LISC, ALL-IDB), reporting consistent gains in IoU, F1, and classification accuracy compared to strong CNN and transformer baselines. An ablation study also demonstrates the individual contributions of saliency preprocessing and cross-layer fusion. SG-CLDFF offers a practical and explainable path toward more reliable automated WBC analysis in clinical workflows.
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            <a href="https://www.alphaxiv.org/abs/2510.17264v1" target="_blank" rel="noopener noreferrer">
                视频中公平且可解释的深度伪造检测
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            Fair and Interpretable Deepfake Detection in Videos
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Akihito Yoshii, Ryosuke Sonoda, Ramya Srinivasan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于深度伪造检测，这属于计算机视觉安全领域，与推荐系统、搜索或广告的核心技术无关。虽然提到了公平性和可解释性，但这些是非技术性主题，明确在无关主题列表中。该研究没有展示与推荐、搜索或广告排名系统的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 07:50:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17264v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17264v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing deepfake detection methods often exhibit bias, lack transparency, and fail to capture temporal information, leading to biased decisions and unreliable results across different demographic groups. In this paper, we propose a fairness-aware deepfake detection framework that integrates temporal feature learning and demographic-aware data augmentation to enhance fairness and interpretability. Our method leverages sequence-based clustering for temporal modeling of deepfake videos and concept extraction to improve detection reliability while also facilitating interpretable decisions for non-expert users. Additionally, we introduce a demography-aware data augmentation method that balances underrepresented groups and applies frequency-domain transformations to preserve deepfake artifacts, thereby mitigating bias and improving generalization. Extensive experiments on FaceForensics++, DFD, Celeb-DF, and DFDC datasets using state-of-the-art (SoTA) architectures (Xception, ResNet) demonstrate the efficacy of the proposed method in obtaining the best tradeoff between fairness and accuracy when compared to SoTA.
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            <a href="https://www.alphaxiv.org/abs/2510.17201v1" target="_blank" rel="noopener noreferrer">
                使用寄存器优化DINOv2用于人脸反欺诈
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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            Optimizing DINOv2 with Registers for Face Anti-Spoofing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mika Feng, Pierre Gallin-Martel, Koichi Ito, Takafumi Aoki
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉领域的人脸反欺诈任务，属于纯粹的视觉应用，与推荐系统、搜索或广告没有明显关联。DINOv2的优化和寄存器技术是针对视觉任务的特定改进，没有展示在推荐、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 06:27:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17201v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17201v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Face recognition systems are designed to be robust against variations in head pose, illumination, and image blur during capture. However, malicious actors can exploit these systems by presenting a face photo of a registered user, potentially bypassing the authentication process. Such spoofing attacks must be detected prior to face recognition. In this paper, we propose a DINOv2-based spoofing attack detection method to discern minute differences between live and spoofed face images. Specifically, we employ DINOv2 with registers to extract generalizable features and to suppress perturbations in the attention mechanism, which enables focused attention on essential and minute features. We demonstrate the effectiveness of the proposed method through experiments conducted on the dataset provided by ``The 6th Face Anti-Spoofing Workshop: Unified Physical-Digital Attacks Detection@ICCV2025'' and SiW dataset.
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            <a href="https://www.alphaxiv.org/abs/2510.17200v1" target="_blank" rel="noopener noreferrer">
                EndoCIL：一种用于内窥镜图像分类的类增量学习框架
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            EndoCIL: A Class-Incremental Learning Framework for Endoscopic Image Classification
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bingrong Liu, Jun Shi, Yushan Zheng
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学内窥镜图像分类，属于医学领域的特定应用，与推荐系统、搜索或广告完全无关。类增量学习虽然是机器学习技术，但在此应用于纯粹的医学图像分析场景，没有任何与RecSys/Search/Ads相关的潜在应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 06:26:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17200v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17200v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Class-incremental learning (CIL) for endoscopic image analysis is crucial for real-world clinical applications, where diagnostic models should continuously adapt to evolving clinical data while retaining performance on previously learned ones. However, existing replay-based CIL methods fail to effectively mitigate catastrophic forgetting due to severe domain discrepancies and class imbalance inherent in endoscopic imaging. To tackle these challenges, we propose EndoCIL, a novel and unified CIL framework specifically tailored for endoscopic image diagnosis. EndoCIL incorporates three key components: Maximum Mean Discrepancy Based Replay (MDBR), employing a distribution-aligned greedy strategy to select diverse and representative exemplars, Prior Regularized Class Balanced Loss (PRCBL), designed to alleviate both inter-phase and intra-phase class imbalance by integrating prior class distributions and balance weights into the loss function, and Calibration of Fully-Connected Gradients (CFG), which adjusts the classifier gradients to mitigate bias toward new classes. Extensive experiments conducted on four public endoscopic datasets demonstrate that EndoCIL generally outperforms state-of-the-art CIL methods across varying buffer sizes and evaluation metrics. The proposed framework effectively balances stability and plasticity in lifelong endoscopic diagnosis, showing promising potential for clinical scalability and deployment.
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            <a href="https://www.alphaxiv.org/abs/2510.17199v1" target="_blank" rel="noopener noreferrer">
                基于视频分析战术特征的VALORANT回合结果预测
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            Round Outcome Prediction in VALORANT Using Tactical Features from Video Analysis
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Nirai Hayakawa, Kazumasa Shimari, Kazuma Yamasaki, Hirotatsu Hoshikawa, Rikuto T...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于游戏VALORANT中的回合结果预测，使用视频分析提取战术特征。这与我的核心关注点（推荐系统、搜索、广告）完全无关，也不涉及LLM技术、Transformer架构进展或异构数据建模。该研究属于游戏分析和计算机视觉领域，没有明显的推荐系统、搜索或广告应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 06:23:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17199v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17199v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recently, research on predicting match outcomes in esports has been actively conducted, but much of it is based on match log data and statistical information. This research targets the FPS game VALORANT, which requires complex strategies, and aims to build a round outcome prediction model by analyzing minimap information in match footage. Specifically, based on the video recognition model TimeSformer, we attempt to improve prediction accuracy by incorporating detailed tactical features extracted from minimap information, such as character position information and other in-game events. This paper reports preliminary results showing that a model trained on a dataset augmented with such tactical event labels achieved approximately 81% prediction accuracy, especially from the middle phases of a round onward, significantly outperforming a model trained on a dataset with the minimap information itself. This suggests that leveraging tactical features from match footage is highly effective for predicting round outcomes in VALORANT.
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            <a href="https://www.alphaxiv.org/abs/2510.17198v1" target="_blank" rel="noopener noreferrer">
                从像素到人群：基于卫星影像的孟加拉国河岸侵蚀与消失村庄的测绘与量化
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            From Pixels to People: Satellite-Based Mapping and Quantification of Riverbank Erosion and Lost Villages in Bangladesh
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>M Saifuzzaman Rafat, Mohd Ruhul Ameen, Akif Islam, Abu Saleh Musa Miah, Jungpil ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于卫星遥感、地理测绘和环境监测领域，与推荐系统、搜索或广告的核心技术无关。论文内容涉及河岸侵蚀和村庄消失的地理量化，属于地球科学和环境工程范畴，没有任何明显的技术关联或潜在应用可以转化到我的关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 06:20:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17198v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17198v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The great rivers of Bangladesh, arteries of commerce and sustenance, are also agents of relentless destruction. Each year, they swallow whole villages and vast tracts of farmland, erasing communities from the map and displacing thousands of families. To track this slow-motion catastrophe has, until now, been a Herculean task for human analysts. Here we show how a powerful general-purpose vision model, the Segment Anything Model (SAM), can be adapted to this task with remarkable precision. To do this, we assembled a new dataset - a digital chronicle of loss compiled from historical Google Earth imagery of Bangladesh's most vulnerable regions, including Mokterer Char Union, Kedarpur Union, Balchipara village, and Chowhali Upazila, from 2003 to 2025. Crucially, this dataset is the first to include manually annotated data on the settlements that have vanished beneath the water. Our method first uses a simple color-channel analysis to provide a rough segmentation of land and water, and then fine-tunes SAM's mask decoder to recognize the subtle signatures of riverbank erosion. The resulting model demonstrates a keen eye for this destructive process, achieving a mean Intersection over Union of 86.30% and a Dice score of 92.60% - a performance that significantly surpasses traditional methods and off-the-shelf deep learning models. This work delivers three key contributions: the first annotated dataset of disappeared settlements in Bangladesh due to river erosion; a specialized AI model fine-tuned for this critical task; and a method for quantifying land loss with compelling visual evidence. Together, these tools provide a powerful new lens through which policymakers and disaster management agencies can monitor erosion, anticipate its trajectory, and ultimately protect the vulnerable communities in its path.
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            <a href="https://www.alphaxiv.org/abs/2510.17181v1" target="_blank" rel="noopener noreferrer">
                从单目视频中通过手部接触捕捉头部虚拟形象
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            Capturing Head Avatar with Hand Contacts from a Monocular Video
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haonan He, Yufeng Zheng, Jie Song
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉和图形学领域的头部虚拟形象生成技术，涉及手部接触和单目视频处理。这与推荐系统、搜索或广告的核心技术领域没有直接关联，也不涉及LLM、Transformer架构或异构数据建模等关键技术。该研究属于纯粹的视觉和图形学应用范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 05:55:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17181v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17181v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Photorealistic 3D head avatars are vital for telepresence, gaming, and VR. However, most methods focus solely on facial regions, ignoring natural hand-face interactions, such as a hand resting on the chin or fingers gently touching the cheek, which convey cognitive states like pondering. In this work, we present a novel framework that jointly learns detailed head avatars and the non-rigid deformations induced by hand-face interactions. There are two principal challenges in this task. First, naively tracking hand and face separately fails to capture their relative poses. To overcome this, we propose to combine depth order loss with contact regularization during pose tracking, ensuring correct spatial relationships between the face and hand. Second, no publicly available priors exist for hand-induced deformations, making them non-trivial to learn from monocular videos. To address this, we learn a PCA basis specific to hand-induced facial deformations from a face-hand interaction dataset. This reduces the problem to estimating a compact set of PCA parameters rather than a full spatial deformation field. Furthermore, inspired by physics-based simulation, we incorporate a contact loss that provides additional supervision, significantly reducing interpenetration artifacts and enhancing the physical plausibility of the results. We evaluate our approach on RGB(D) videos captured by an iPhone. Additionally, to better evaluate the reconstructed geometry, we construct a synthetic dataset of avatars with various types of hand interactions. We show that our method can capture better appearance and more accurate deforming geometry of the face than SOTA surface reconstruction methods.
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            <a href="https://www.alphaxiv.org/abs/2510.17179v1" target="_blank" rel="noopener noreferrer">
                浮游生物识别中的分布外检测基准测试：海洋生态监测中先进方法的系统评估
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            Benchmarking Out-of-Distribution Detection for Plankton Recognition: A Systematic Evaluation of Advanced Methods in Marine Ecological Monitoring
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yingzi Han, Jiakai He, Chuanlong Xie, Jianping Li
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于海洋生态监测中的浮游生物识别和分布外检测，属于生物学和海洋科学领域的具体应用。虽然涉及检测和识别技术，但缺乏与推荐系统、搜索或广告领域的直接关联，也不涉及LLM、Transformer架构或异质数据统一建模等核心技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 05:50:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17179v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17179v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Automated plankton recognition models face significant challenges during real-world deployment due to distribution shifts (Out-of-Distribution, OoD) between training and test data. This stems from plankton's complex morphologies, vast species diversity, and the continuous discovery of novel species, which leads to unpredictable errors during inference. Despite rapid advancements in OoD detection methods in recent years, the field of plankton recognition still lacks a systematic integration of the latest computer vision developments and a unified benchmark for large-scale evaluation. To address this, this paper meticulously designed a series of OoD benchmarks simulating various distribution shift scenarios based on the DYB-PlanktonNet dataset \cite{875n-f104-21}, and systematically evaluated twenty-two OoD detection methods. Extensive experimental results demonstrate that the ViM \cite{wang2022vim} method significantly outperforms other approaches in our constructed benchmarks, particularly excelling in Far-OoD scenarios with substantial improvements in key metrics. This comprehensive evaluation not only provides a reliable reference for algorithm selection in automated plankton recognition but also lays a solid foundation for future research in plankton OoD detection. To our knowledge, this study marks the first large-scale, systematic evaluation and analysis of Out-of-Distribution data detection methods in plankton recognition. Code is available at https://github.com/BlackJack0083/PlanktonOoD.
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            <a href="https://www.alphaxiv.org/abs/2510.17169v1" target="_blank" rel="noopener noreferrer">
                研究针对黑盒人脸识别中使用的预处理的对抗鲁棒性
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            Investigating Adversarial Robustness against Preprocessing used in Blackbox Face Recognition
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Roland Croft, Brian Du, Darcy Joseph, Sharath Kumar
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉领域的人脸识别系统对抗攻击，属于纯粹的视觉安全研究。虽然提到了预处理技术，但主要关注安全漏洞而非推荐系统、搜索或广告的核心技术。论文内容与我的关注领域（推荐系统、搜索、广告、LLM技术、Transformer架构等）没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 05:19:35
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17169v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17169v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Face Recognition (FR) models have been shown to be vulnerable to adversarial examples that subtly alter benign facial images, exposing blind spots in these systems, as well as protecting user privacy. End-to-end FR systems first obtain preprocessed faces from diverse facial imagery prior to computing the similarity of the deep feature embeddings. Whilst face preprocessing is a critical component of FR systems, and hence adversarial attacks against them, we observe that this preprocessing is often overlooked in blackbox settings. Our study seeks to investigate the transferability of several out-of-the-box state-of-the-art adversarial attacks against FR when applied against different preprocessing techniques used in a blackbox setting. We observe that the choice of face detection model can degrade the attack success rate by up to 78%, whereas choice of interpolation method during downsampling has relatively minimal impacts. Furthermore, we find that the requirement for facial preprocessing even degrades attack strength in a whitebox setting, due to the unintended interaction of produced noise vectors against face detection models. Based on these findings, we propose a preprocessing-invariant method using input transformations that improves the transferability of the studied attacks by up to 27%. Our findings highlight the importance of preprocessing in FR systems, and the need for its consideration towards improving the adversarial generalisation of facial adversarial examples.
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            <a href="https://www.alphaxiv.org/abs/2510.17157v1" target="_blank" rel="noopener noreferrer">
                GACO-CAD：基于单图像的几何增强与简洁性优化的CAD模型生成
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            GACO-CAD: Geometry-Augmented and Conciseness-Optimized CAD Model Generation from Single Image
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yinghui Wang, Xinyu Zhang, Peng Du
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机辅助设计(CAD)模型的生成，属于计算机图形学领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。虽然标题中提到几何增强和简洁性优化，但这些技术主要应用于3D建模和工程设计，无法直接应用于推荐、搜索或广告中的异构数据处理或Transformer架构改进。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 04:57:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17157v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17157v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Generating editable, parametric CAD models from a single image holds great potential to lower the barriers of industrial concept design. However, current multi-modal large language models (MLLMs) still struggle with accurately inferring 3D geometry from 2D images due to limited spatial reasoning capabilities. We address this limitation by introducing GACO-CAD, a novel two-stage post-training framework. It is designed to achieve a joint objective: simultaneously improving the geometric accuracy of the generated CAD models and encouraging the use of more concise modeling procedures. First, during supervised fine-tuning, we leverage depth and surface normal maps as dense geometric priors, combining them with the RGB image to form a multi-channel input. In the context of single-view reconstruction, these priors provide complementary spatial cues that help the MLLM more reliably recover 3D geometry from 2D observations. Second, during reinforcement learning, we introduce a group length reward that, while preserving high geometric fidelity, promotes the generation of more compact and less redundant parametric modeling sequences. A simple dynamic weighting strategy is adopted to stabilize training. Experiments on the DeepCAD and Fusion360 datasets show that GACO-CAD achieves state-of-the-art performance under the same MLLM backbone, consistently outperforming existing methods in terms of code validity, geometric accuracy, and modeling conciseness.
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            <a href="https://www.alphaxiv.org/abs/2510.17148v1" target="_blank" rel="noopener noreferrer">
                DiffVLA++：通过度量引导对齐桥接认知推理与端到端驾驶
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            DiffVLA++: Bridging Cognitive Reasoning and End-to-End Driving through Metric-Guided Alignment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yu Gao, Yiru Wang, Anqing Jiang, Heng Yuwen, Wang Shuo, Sun Hao, Wang Jijun
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自动驾驶领域，涉及认知推理和端到端驾驶技术。虽然提到了对齐概念，但其核心应用场景与推荐系统、搜索或广告完全无关，属于纯粹的自动驾驶研究，没有任何潜在的应用于RecSys/Search/Ads的可能性。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 04:49:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17148v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17148v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                    Conventional end-to-end (E2E) driving models are effective at generating physically plausible trajectories, but often fail to generalize to long-tail scenarios due to the lack of essential world knowledge to understand and reason about surrounding environments. In contrast, Vision-Language-Action (VLA) models leverage world knowledge to handle challenging cases, but their limited 3D reasoning capability can lead to physically infeasible actions. In this work we introduce DiffVLA++, an enhanced autonomous driving framework that explicitly bridges cognitive reasoning and E2E planning through metric-guided alignment. First, we build a VLA module directly generating semantically grounded driving trajectories. Second, we design an E2E module with a dense trajectory vocabulary that ensures physical feasibility. Third, and most critically, we introduce a metric-guided trajectory scorer that guides and aligns the outputs of the VLA and E2E modules, thereby integrating their complementary strengths. The experiment on the ICCV 2025 Autonomous Grand Challenge leaderboard shows that DiffVLA++ achieves EPDMS of 49.12.
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            <a href="https://www.alphaxiv.org/abs/2510.17131v1" target="_blank" rel="noopener noreferrer">
                GOOD：用于分布外检测的无训练引导扩散采样
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            GOOD: Training-Free Guided Diffusion Sampling for Out-of-Distribution Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xin Gao, Jiyao Liu, Guanghao Li, Yueming Lyu, Jianxiong Gao, Weichen Yu, Ningshe...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于扩散模型和分布外检测，属于计算机视觉和生成模型领域。虽然扩散模型是生成技术的一种，但该论文的应用场景（分布外检测）与推荐系统、搜索或广告的核心技术需求没有直接关联，也不涉及Transformer架构改进或LLM技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 03:58:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17131v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17131v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Recent advancements have explored text-to-image diffusion models for synthesizing out-of-distribution (OOD) samples, substantially enhancing the performance of OOD detection. However, existing approaches typically rely on perturbing text-conditioned embeddings, resulting in semantic instability and insufficient shift diversity, which limit generalization to realistic OOD. To address these challenges, we propose GOOD, a novel and flexible framework that directly guides diffusion sampling trajectories towards OOD regions using off-the-shelf in-distribution (ID) classifiers. GOOD incorporates dual-level guidance: (1) Image-level guidance based on the gradient of log partition to reduce input likelihood, drives samples toward low-density regions in pixel space. (2) Feature-level guidance, derived from k-NN distance in the classifier's latent space, promotes sampling in feature-sparse regions. Hence, this dual-guidance design enables more controllable and diverse OOD sample generation. Additionally, we introduce a unified OOD score that adaptively combines image and feature discrepancies, enhancing detection robustness. We perform thorough quantitative and qualitative analyses to evaluate the effectiveness of GOOD, demonstrating that training with samples generated by GOOD can notably enhance OOD detection performance.
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            <a href="https://www.alphaxiv.org/abs/2510.17114v1" target="_blank" rel="noopener noreferrer">
                面向消费级摄像头的基于环境光照的不可感知水印技术
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            Towards Imperceptible Watermarking Via Environment Illumination for Consumer Cameras
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hodaka Kawachi, Tomoya Nakamura, Hiroaki Santo, SaiKiran Kumar Tedla, Trevor Dal...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及数字水印技术，属于安全与隐私保护领域，与我的关注点无关。水印技术主要用于内容认证和版权保护，在推荐系统、搜索或广告中没有直接应用潜力。该研究方向属于明确排除的'非技术性主题'范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 03:02:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17114v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17114v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    This paper introduces a method for using LED-based environmental lighting to produce visually imperceptible watermarks for consumer cameras. Our approach optimizes an LED light source's spectral profile to be minimally visible to the human eye while remaining highly detectable by typical consumer cameras. The method jointly considers the human visual system's sensitivity to visible spectra, modern consumer camera sensors' spectral sensitivity, and narrowband LEDs' ability to generate broadband spectra perceived as "white light" (specifically, D65 illumination). To ensure imperceptibility, we employ spectral modulation rather than intensity modulation. Unlike conventional visible light communication, our approach enables watermark extraction at standard low frame rates (30-60 fps). While the information transfer rate is modest-embedding 128 bits within a 10-second video clip-this capacity is sufficient for essential metadata supporting privacy protection and content verification.
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            <a href="https://www.alphaxiv.org/abs/2510.17105v1" target="_blank" rel="noopener noreferrer">
                通过条件细化提升基于预训练扩散模型的低光照图像增强的保真度
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            Boosting Fidelity for Pre-Trained-Diffusion-Based Low-Light Image Enhancement via Condition Refinement
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaogang Xu, Jian Wang, Yunfan Lu, Ruihang Chu, Ruixing Wang, Jiafei Wu, Bei Yu,...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的低光照图像增强技术，使用预训练扩散模型进行图像处理。虽然涉及生成模型，但这是纯粹的视觉增强应用，没有明确的推荐系统、搜索或广告应用场景，也不涉及Transformer架构或异构数据建模。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 02:40:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17105v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17105v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Diffusion-based methods, leveraging pre-trained large models like Stable Diffusion via ControlNet, have achieved remarkable performance in several low-level vision tasks. However, Pre-Trained Diffusion-Based (PTDB) methods often sacrifice content fidelity to attain higher perceptual realism. This issue is exacerbated in low-light scenarios, where severely degraded information caused by the darkness limits effective control. We identify two primary causes of fidelity loss: the absence of suitable conditional latent modeling and the lack of bidirectional interaction between the conditional latent and noisy latent in the diffusion process. To address this, we propose a novel optimization strategy for conditioning in pre-trained diffusion models, enhancing fidelity while preserving realism and aesthetics. Our method introduces a mechanism to recover spatial details lost during VAE encoding, i.e., a latent refinement pipeline incorporating generative priors. Additionally, the refined latent condition interacts dynamically with the noisy latent, leading to improved restoration performance. Our approach is plug-and-play, seamlessly integrating into existing diffusion networks to provide more effective control. Extensive experiments demonstrate significant fidelity improvements in PTDB methods.
                </div>
            </details>
    </div>
</div><!--
 * @Author: Doragd doragd@users.noreply.github.com
 * @Date: 2025-10-09 23:23:38
 * @LastEditors: Doragd doragd@users.noreply.github.com
 * @LastEditTime: 2025-10-10 00:41:41
 * @FilePath: /Algorithm-Practice-in-Industry/paperBotV2/frontend/templates/normal_paper_template.html
 * @Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
-->
<div class="simple-paper-card p-3 collapsed-level-2">
    <div class="flex justify-between items-start mb-1">
        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17095v1" target="_blank" rel="noopener noreferrer">
                GSPlane：通过结构化表示实现简洁而准确的平面重建
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            GSPlane: Concise and Accurate Planar Reconstruction via Structured Representation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruitong Gan, Junran Peng, Yang Liu, Chuanchen Luo, Qing Li, Zhaoxiang Zhang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的平面重建技术，属于纯粹的3D视觉研究领域。虽然标题提到结构化表示，但这与推荐系统、搜索或广告的核心技术需求没有直接关联，也不涉及LLM、Transformer架构或异构数据建模等当前关注的技术方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 01:59:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17095v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17095v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Planes are fundamental primitives of 3D sences, especially in man-made environments such as indoor spaces and urban streets. Representing these planes in a structured and parameterized format facilitates scene editing and physical simulations in downstream applications. Recently, Gaussian Splatting (GS) has demonstrated remarkable effectiveness in the Novel View Synthesis task, with extensions showing great potential in accurate surface reconstruction. However, even state-of-the-art GS representations often struggle to reconstruct planar regions with sufficient smoothness and precision. To address this issue, we propose GSPlane, which recovers accurate geometry and produces clean and well-structured mesh connectivity for plane regions in the reconstructed scene. By leveraging off-the-shelf segmentation and normal prediction models, GSPlane extracts robust planar priors to establish structured representations for planar Gaussian coordinates, which help guide the training process by enforcing geometric consistency. To further enhance training robustness, a Dynamic Gaussian Re-classifier is introduced to adaptively reclassify planar Gaussians with persistently high gradients as non-planar, ensuring more reliable optimization. Furthermore, we utilize the optimized planar priors to refine the mesh layouts, significantly improving topological structure while reducing the number of vertices and faces. We also explore applications of the structured planar representation, which enable decoupling and flexible manipulation of objects on supportive planes. Extensive experiments demonstrate that, with no sacrifice in rendering quality, the introduction of planar priors significantly improves the geometric accuracy of the extracted meshes across various baselines.
                </div>
            </details>
    </div>
</div><!--
 * @Author: Doragd doragd@users.noreply.github.com
 * @Date: 2025-10-09 23:23:38
 * @LastEditors: Doragd doragd@users.noreply.github.com
 * @LastEditTime: 2025-10-10 00:41:41
 * @FilePath: /Algorithm-Practice-in-Industry/paperBotV2/frontend/templates/normal_paper_template.html
 * @Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
-->
<div class="simple-paper-card p-3 collapsed-level-2">
    <div class="flex justify-between items-start mb-1">
        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2510.17068v1" target="_blank" rel="noopener noreferrer">
                ProDAT：用于点云编码的渐进式密度感知尾部丢弃方法
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            ProDAT: Progressive Density-Aware Tail-Drop for Point Cloud Coding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhe Luo, Wenjing Jia, Stuart Perry
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于点云编码技术，属于3D视觉和图形处理领域。点云处理与推荐系统、搜索或广告的核心技术栈没有直接关联，也不涉及Transformer架构改进或LLM技术应用。点云编码主要应用于计算机视觉、自动驾驶等场景，不符合当前关注的任何技术方向。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-10-20 00:50:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2510.17068v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2510.17068v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Three-dimensional (3D) point clouds are becoming increasingly vital in applications such as autonomous driving, augmented reality, and immersive communication, demanding real-time processing and low latency. However, their large data volumes and bandwidth constraints hinder the deployment of high-quality services in resource-limited environments. Progres- sive coding, which allows for decoding at varying levels of detail, provides an alternative by allowing initial partial decoding with subsequent refinement. Although recent learning-based point cloud geometry coding methods have achieved notable success, their fixed latent representation does not support progressive decoding. To bridge this gap, we propose ProDAT, a novel density-aware tail-drop mechanism for progressive point cloud coding. By leveraging density information as a guidance signal, latent features and coordinates are decoded adaptively based on their significance, therefore achieving progressive decoding at multiple bitrates using one single model. Experimental results on benchmark datasets show that the proposed ProDAT not only enables progressive coding but also achieves superior coding efficiency compared to state-of-the-art learning-based coding techniques, with over 28.6% BD-rate improvement for PSNR- D2 on SemanticKITTI and over 18.15% for ShapeNet
                </div>
            </details>
    </div>
</div>
        </div>
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                        // 构造目标URL并跳转
                        const targetUrl = 'arxiv_' + dateStr + '.html';
                        window.location.href = targetUrl;
                    });
                } else {
                    // 没有论文数据的日期样式（置灰不可点击）
                    dayElement.classList.add('py-1', 'text-gray-400', 'cursor-not-allowed');
                }
                
                // 高亮显示当天日期（覆盖之前的样式）
                if (currentDateObj.getTime() === today.getTime()) {
                    dayElement.classList.remove('bg-blue-50');
                    dayElement.classList.add('bg-primary', 'text-white', 'font-bold', 'shadow');
                    if (!hasPapers) {
                        // 当天没有论文时，仍然置灰但保持背景色
                        dayElement.classList.add('opacity-70');
                    }
                }
                
                // 高亮显示当前选中的日期
                if (displayDateStr === selectedDateText.textContent) {
                    dayElement.classList.add('font-bold', 'border-2', 'border-primary', 'rounded-lg', 'shadow-md');
                }
                
                // 增强有论文数据的日期样式，使其更明显
                if (hasPapers && currentDateObj.getTime() !== today.getTime()) {
                    dayElement.classList.add('bg-blue-100', 'hover:bg-blue-200', 'transition-colors', 'duration-200');
                }
                
                calendarGrid.appendChild(dayElement);
            }
        }
    }
    </script>
    </body>

</html>